{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "83ca5b9f-1a31-41e6-a0f7-2dc30752b09b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from tensorflow.keras.layers import Input, Conv1D, MaxPooling1D, Dense, Add, Dropout, Flatten, TimeDistributed\n",
    "from tensorflow.keras.models import Model, Sequential\n",
    "from tensorflow.keras import activations, optimizers, regularizers\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "import tensorflow.keras.backend as kb\n",
    "from tensorflow import keras\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "from time import *\n",
    "user_balance = pd.read_csv(r'D:\\机器学习实验\\Purchase Redemption Data\\user_balance_table.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d5eb38ef-08a6-4d1c-80bf-2adc8a0a6322",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tmp = user_balance.groupby(['report_date'])[['total_purchase_amt', 'total_redeem_amt']].sum()\n",
    "df_tmp.index = pd.to_datetime(df_tmp.index, format='%Y%m%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f4d7dcb6-0800-427e-b5bb-0c1c813d17ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "holidays = ('20130813', '20130902', '20131001', '20131111', '20130919', '20131225', '20140101', '20140130', '20140131',\n",
    "           '20140214', '20140405', '20140501', '20140602', '20140802', '20140901', '20140908')\n",
    "def create_features(timeindex):\n",
    "    n = len(timeindex)\n",
    "    features = np.zeros((n, 4))\n",
    "    features[:, 0] = timeindex.day.values/31\n",
    "    features[:, 1] = timeindex.month.values/12\n",
    "    features[:, 2] = timeindex.weekday.values/6\n",
    "    for i in range(n):\n",
    "        if timeindex[i].strftime('%Y%m%d') in holidays:\n",
    "            features[i, 3] = 1\n",
    "    return features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "912fcd58-83e3-46f9-b81e-d0c9ab43c294",
   "metadata": {},
   "outputs": [],
   "source": [
    "holidays = ('20130813', '20130902', '20131001', '20131111', '20130919', '20131225', '20140101', '20140130', '20140131',\n",
    "           '20140214', '20140405', '20140501', '20140602', '20140802', '20140901', '20140908')\n",
    "def create_features(timeindex):\n",
    "    n = len(timeindex)\n",
    "    features = np.zeros((n, 4))\n",
    "    features[:, 0] = timeindex.day.values/31\n",
    "    features[:, 1] = timeindex.month.values/12\n",
    "    features[:, 2] = timeindex.weekday.values/6\n",
    "    for i in range(n):\n",
    "        if timeindex[i].strftime('%Y%m%d') in holidays:\n",
    "            features[i, 3] = 1\n",
    "    return features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fe2d9f26-ea23-410c-9173-94805a451195",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = create_features(df_tmp.index)\n",
    "september = pd.to_datetime(['201409%02d' % i for i in range(1, 31)])\n",
    "features_sep = create_features(september)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "716d4d44-dc69-4e41-b8fb-d1dc06b93403",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler_pur = MinMaxScaler()\n",
    "scaler_red = MinMaxScaler()\n",
    "data_pur = scaler_pur.fit_transform(df_tmp.values[:, 0:1])\n",
    "data_red = scaler_red.fit_transform(df_tmp.values[:, 1:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0c9a3f7e-fba2-490c-9653-7e165d709ac1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dataset(data, back, forward=30):\n",
    "    n_samples = len(data) - back - forward + 1\n",
    "    X, Y = np.zeros((n_samples, back, data.shape[-1])), np.zeros((n_samples, forward, data.shape[-1]))\n",
    "    for i in range(n_samples):\n",
    "        X[i, ...] = data[i:i+back, :]\n",
    "        Y[i, ...] = data[i+back:i+back+forward, :]\n",
    "    return X, Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0c59d8b2-ee93-404d-846c-cfb927fb12d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_cnn(X_trn, lr, n_outputs, dropout_rate):\n",
    "    inputs = Input(X_trn.shape[1:])\n",
    "    z = Conv1D(64, 14, padding='valid', activation='relu', kernel_initializer='he_uniform')(inputs)\n",
    "#     z = MaxPooling1D(2)(z)\n",
    "    z = Conv1D(128, 7, padding='valid', activation='relu', kernel_initializer='he_uniform')(z)\n",
    "    z = MaxPooling1D(2)(z)\n",
    "    z = Conv1D(256, 3, padding='valid', activation='relu', kernel_initializer='he_uniform')(z)\n",
    "    z = Conv1D(256, 3, padding='valid', activation='relu', kernel_initializer='he_uniform')(z)\n",
    "    z = MaxPooling1D(2)(z)\n",
    "    z = Flatten()(z)\n",
    "    z = Dropout(dropout_rate)(z)\n",
    "    z = Dense(128, activation='relu', kernel_initializer='he_uniform')(z)\n",
    "    z = Dropout(dropout_rate)(z)\n",
    "    z = Dense(84, activation='relu', kernel_initializer='he_uniform')(z)\n",
    "    outputs = Dense(n_outputs)(z)\n",
    "    model = Model(inputs=inputs, outputs=outputs)\n",
    "    adam = optimizers.Adam(learning_rate=lr)\n",
    "    model.compile(loss='mse', optimizer=adam, metrics=['mae'])\n",
    "    model.summary()\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7c279699-c6e6-4fa6-87ee-2acdb77e7205",
   "metadata": {},
   "outputs": [],
   "source": [
    "back = 60\n",
    "forward = 30\n",
    "X_pur_data, Y_pur_data = create_dataset(data_pur, back, forward)\n",
    "X_red_data, Y_red_data = create_dataset(data_red, back, forward)\n",
    "X_features, Y_features = create_dataset(features, back, forward)\n",
    "Y_features = np.concatenate((Y_features, np.zeros((Y_features.shape[0], back-forward, Y_features.shape[-1]))), axis=1)\n",
    "# X_pur, X_red = np.concatenate((X_pur_data, X_features, Y_features), axis=-1), np.concatenate((X_red_data, X_features, Y_features), axis=-1)\n",
    "# X_pur_trn, X_pur_val, X_red_trn, X_red_val = X_pur[:-forward, ...], X_pur[-1:, ...], X_red[:-forward, ...], X_red[-1:, ...]\n",
    "# Y_pur_trn, Y_pur_val, Y_red_trn, Y_red_val = Y_pur_data[:-forward, ...], Y_pur_data[-1:, ...], Y_red_data[:-forward, ...], Y_red_data[-1:, ...]\n",
    "Y_fea_sep = np.concatenate((features_sep, np.zeros((back-forward, features_sep.shape[-1]))), axis=0)\n",
    "# X_pur_tst = np.concatenate((data_pur[-back:, :], features[-back:, :], Y_fea_sep), axis=-1)[None, ...]\n",
    "# X_red_tst = np.concatenate((data_red[-back:, :], features[-back:, :], Y_fea_sep), axis=-1)[None, ...]\n",
    "X = np.concatenate((X_pur_data, X_red_data, X_features, Y_features), axis=-1)\n",
    "Y = np.concatenate((Y_pur_data, Y_red_data), axis=1)\n",
    "X_trn, X_val, Y_trn, Y_val = X[:-forward, ...], X[-1:, ...], Y[:-forward, ...], Y[-1:, ...]\n",
    "X_tst = np.concatenate((data_pur[-back:, :], data_red[-back:, :], features[-back:, :], Y_fea_sep), axis=-1)[None, ...]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1caea1a0-03a1-4d57-8e87-6e29dd6a2d70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"functional\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ input_layer (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">60</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)              │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv1d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv1D</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">9,024</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv1d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv1D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">41</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)             │          <span style=\"color: #00af00; text-decoration-color: #00af00\">57,472</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling1d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling1D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)             │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv1d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv1D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">18</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)             │          <span style=\"color: #00af00; text-decoration-color: #00af00\">98,560</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv1d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv1D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">196,864</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling1d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling1D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2048</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2048</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │         <span style=\"color: #00af00; text-decoration-color: #00af00\">262,272</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">84</span>)                  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">10,836</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">60</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">5,100</span> │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ input_layer (\u001b[38;5;33mInputLayer\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m60\u001b[0m, \u001b[38;5;34m10\u001b[0m)              │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv1d (\u001b[38;5;33mConv1D\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m64\u001b[0m)              │           \u001b[38;5;34m9,024\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv1d_1 (\u001b[38;5;33mConv1D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m41\u001b[0m, \u001b[38;5;34m128\u001b[0m)             │          \u001b[38;5;34m57,472\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling1d (\u001b[38;5;33mMaxPooling1D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m128\u001b[0m)             │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv1d_2 (\u001b[38;5;33mConv1D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m18\u001b[0m, \u001b[38;5;34m256\u001b[0m)             │          \u001b[38;5;34m98,560\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv1d_3 (\u001b[38;5;33mConv1D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m256\u001b[0m)             │         \u001b[38;5;34m196,864\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling1d_1 (\u001b[38;5;33mMaxPooling1D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m)              │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2048\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout (\u001b[38;5;33mDropout\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2048\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │         \u001b[38;5;34m262,272\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m84\u001b[0m)                  │          \u001b[38;5;34m10,836\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m60\u001b[0m)                  │           \u001b[38;5;34m5,100\u001b[0m │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">640,128</span> (2.44 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m640,128\u001b[0m (2.44 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">640,128</span> (2.44 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m640,128\u001b[0m (2.44 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      "10/10 - 5s - 540ms/step - loss: 0.3546 - mae: 0.4122 - val_loss: 0.1322 - val_mae: 0.3302\n",
      "Epoch 2/1000\n",
      "10/10 - 0s - 42ms/step - loss: 0.1104 - mae: 0.2609 - val_loss: 0.0747 - val_mae: 0.2160\n",
      "Epoch 3/1000\n",
      "10/10 - 0s - 38ms/step - loss: 0.0821 - mae: 0.2146 - val_loss: 0.0585 - val_mae: 0.1882\n",
      "Epoch 4/1000\n",
      "10/10 - 0s - 39ms/step - loss: 0.0618 - mae: 0.1829 - val_loss: 0.0392 - val_mae: 0.1510\n",
      "Epoch 5/1000\n",
      "10/10 - 0s - 40ms/step - loss: 0.0487 - mae: 0.1610 - val_loss: 0.0268 - val_mae: 0.1226\n",
      "Epoch 6/1000\n",
      "10/10 - 0s - 37ms/step - loss: 0.0412 - mae: 0.1464 - val_loss: 0.0180 - val_mae: 0.1030\n",
      "Epoch 7/1000\n",
      "10/10 - 0s - 39ms/step - loss: 0.0353 - mae: 0.1360 - val_loss: 0.0155 - val_mae: 0.0935\n",
      "Epoch 8/1000\n",
      "10/10 - 0s - 39ms/step - loss: 0.0319 - mae: 0.1292 - val_loss: 0.0150 - val_mae: 0.0910\n",
      "Epoch 9/1000\n",
      "10/10 - 0s - 39ms/step - loss: 0.0311 - mae: 0.1262 - val_loss: 0.0169 - val_mae: 0.1051\n",
      "Epoch 10/1000\n",
      "10/10 - 0s - 40ms/step - loss: 0.0306 - mae: 0.1264 - val_loss: 0.0138 - val_mae: 0.0879\n",
      "Epoch 11/1000\n",
      "10/10 - 0s - 39ms/step - loss: 0.0291 - mae: 0.1218 - val_loss: 0.0145 - val_mae: 0.0954\n",
      "Epoch 12/1000\n",
      "10/10 - 0s - 37ms/step - loss: 0.0274 - mae: 0.1194 - val_loss: 0.0142 - val_mae: 0.0850\n",
      "Epoch 13/1000\n",
      "10/10 - 0s - 40ms/step - loss: 0.0279 - mae: 0.1192 - val_loss: 0.0135 - val_mae: 0.0832\n",
      "Epoch 14/1000\n",
      "10/10 - 0s - 36ms/step - loss: 0.0262 - mae: 0.1163 - val_loss: 0.0133 - val_mae: 0.0829\n",
      "Epoch 15/1000\n",
      "10/10 - 0s - 40ms/step - loss: 0.0253 - mae: 0.1142 - val_loss: 0.0170 - val_mae: 0.1096\n",
      "Epoch 16/1000\n",
      "10/10 - 0s - 39ms/step - loss: 0.0272 - mae: 0.1188 - val_loss: 0.0131 - val_mae: 0.0814\n",
      "Epoch 17/1000\n",
      "10/10 - 0s - 40ms/step - loss: 0.0252 - mae: 0.1141 - val_loss: 0.0144 - val_mae: 0.0820\n",
      "Epoch 18/1000\n",
      "10/10 - 0s - 37ms/step - loss: 0.0255 - mae: 0.1146 - val_loss: 0.0129 - val_mae: 0.0799\n",
      "Epoch 19/1000\n",
      "10/10 - 0s - 40ms/step - loss: 0.0247 - mae: 0.1135 - val_loss: 0.0126 - val_mae: 0.0838\n",
      "Epoch 20/1000\n",
      "10/10 - 0s - 42ms/step - loss: 0.0240 - mae: 0.1120 - val_loss: 0.0151 - val_mae: 0.0992\n",
      "Epoch 21/1000\n",
      "10/10 - 0s - 40ms/step - loss: 0.0240 - mae: 0.1115 - val_loss: 0.0168 - val_mae: 0.1067\n",
      "Epoch 22/1000\n",
      "10/10 - 0s - 35ms/step - loss: 0.0240 - mae: 0.1119 - val_loss: 0.0140 - val_mae: 0.0942\n",
      "Epoch 23/1000\n",
      "10/10 - 0s - 37ms/step - loss: 0.0245 - mae: 0.1133 - val_loss: 0.0128 - val_mae: 0.0891\n",
      "Epoch 24/1000\n",
      "10/10 - 0s - 39ms/step - loss: 0.0229 - mae: 0.1090 - val_loss: 0.0114 - val_mae: 0.0805\n",
      "Epoch 25/1000\n",
      "10/10 - 0s - 39ms/step - loss: 0.0222 - mae: 0.1079 - val_loss: 0.0123 - val_mae: 0.0879\n",
      "Epoch 26/1000\n",
      "10/10 - 0s - 41ms/step - loss: 0.0221 - mae: 0.1076 - val_loss: 0.0123 - val_mae: 0.0894\n",
      "Epoch 27/1000\n",
      "10/10 - 0s - 40ms/step - loss: 0.0219 - mae: 0.1066 - val_loss: 0.0101 - val_mae: 0.0762\n",
      "Epoch 28/1000\n",
      "10/10 - 0s - 40ms/step - loss: 0.0218 - mae: 0.1064 - val_loss: 0.0099 - val_mae: 0.0730\n",
      "Epoch 29/1000\n",
      "10/10 - 0s - 40ms/step - loss: 0.0218 - mae: 0.1064 - val_loss: 0.0099 - val_mae: 0.0698\n",
      "Epoch 30/1000\n",
      "10/10 - 0s - 39ms/step - loss: 0.0216 - mae: 0.1058 - val_loss: 0.0099 - val_mae: 0.0713\n",
      "Epoch 31/1000\n",
      "10/10 - 0s - 38ms/step - loss: 0.0214 - mae: 0.1049 - val_loss: 0.0108 - val_mae: 0.0793\n",
      "Epoch 32/1000\n",
      "10/10 - 0s - 39ms/step - loss: 0.0208 - mae: 0.1039 - val_loss: 0.0125 - val_mae: 0.0915\n",
      "Epoch 33/1000\n",
      "10/10 - 0s - 37ms/step - loss: 0.0212 - mae: 0.1042 - val_loss: 0.0116 - val_mae: 0.0868\n",
      "Epoch 34/1000\n",
      "10/10 - 0s - 38ms/step - loss: 0.0211 - mae: 0.1044 - val_loss: 0.0094 - val_mae: 0.0701\n",
      "Epoch 35/1000\n",
      "10/10 - 0s - 35ms/step - loss: 0.0202 - mae: 0.1026 - val_loss: 0.0096 - val_mae: 0.0742\n",
      "Epoch 36/1000\n",
      "10/10 - 0s - 39ms/step - loss: 0.0196 - mae: 0.1005 - val_loss: 0.0102 - val_mae: 0.0793\n",
      "Epoch 37/1000\n",
      "10/10 - 0s - 38ms/step - loss: 0.0195 - mae: 0.1005 - val_loss: 0.0095 - val_mae: 0.0745\n",
      "Epoch 38/1000\n",
      "10/10 - 0s - 40ms/step - loss: 0.0198 - mae: 0.1021 - val_loss: 0.0107 - val_mae: 0.0830\n",
      "Epoch 39/1000\n",
      "10/10 - 0s - 37ms/step - loss: 0.0189 - mae: 0.0986 - val_loss: 0.0093 - val_mae: 0.0725\n",
      "Epoch 40/1000\n",
      "10/10 - 0s - 38ms/step - loss: 0.0189 - mae: 0.0990 - val_loss: 0.0090 - val_mae: 0.0719\n",
      "Epoch 41/1000\n",
      "10/10 - 0s - 39ms/step - loss: 0.0189 - mae: 0.0987 - val_loss: 0.0085 - val_mae: 0.0684\n",
      "Epoch 42/1000\n",
      "10/10 - 0s - 35ms/step - loss: 0.0186 - mae: 0.0976 - val_loss: 0.0095 - val_mae: 0.0758\n",
      "Epoch 43/1000\n",
      "10/10 - 0s - 37ms/step - loss: 0.0184 - mae: 0.0972 - val_loss: 0.0089 - val_mae: 0.0712\n",
      "Epoch 44/1000\n",
      "10/10 - 0s - 38ms/step - loss: 0.0183 - mae: 0.0969 - val_loss: 0.0083 - val_mae: 0.0675\n",
      "Epoch 45/1000\n",
      "10/10 - 0s - 39ms/step - loss: 0.0177 - mae: 0.0958 - val_loss: 0.0099 - val_mae: 0.0806\n",
      "Epoch 46/1000\n",
      "10/10 - 0s - 39ms/step - loss: 0.0178 - mae: 0.0952 - val_loss: 0.0097 - val_mae: 0.0781\n",
      "Epoch 47/1000\n",
      "10/10 - 0s - 37ms/step - loss: 0.0176 - mae: 0.0946 - val_loss: 0.0105 - val_mae: 0.0817\n",
      "Epoch 48/1000\n",
      "10/10 - 0s - 38ms/step - loss: 0.0177 - mae: 0.0956 - val_loss: 0.0088 - val_mae: 0.0721\n",
      "Epoch 49/1000\n",
      "10/10 - 0s - 37ms/step - loss: 0.0174 - mae: 0.0944 - val_loss: 0.0100 - val_mae: 0.0804\n",
      "Epoch 50/1000\n",
      "10/10 - 0s - 37ms/step - loss: 0.0174 - mae: 0.0949 - val_loss: 0.0105 - val_mae: 0.0820\n",
      "Epoch 51/1000\n",
      "10/10 - 0s - 36ms/step - loss: 0.0168 - mae: 0.0929 - val_loss: 0.0094 - val_mae: 0.0761\n",
      "Epoch 52/1000\n",
      "10/10 - 0s - 36ms/step - loss: 0.0169 - mae: 0.0931 - val_loss: 0.0091 - val_mae: 0.0738\n",
      "Epoch 53/1000\n",
      "10/10 - 0s - 36ms/step - loss: 0.0169 - mae: 0.0935 - val_loss: 0.0084 - val_mae: 0.0685\n",
      "Epoch 54/1000\n",
      "10/10 - 0s - 37ms/step - loss: 0.0165 - mae: 0.0922 - val_loss: 0.0080 - val_mae: 0.0637\n",
      "Epoch 55/1000\n",
      "10/10 - 0s - 35ms/step - loss: 0.0168 - mae: 0.0926 - val_loss: 0.0082 - val_mae: 0.0652\n",
      "Epoch 56/1000\n",
      "10/10 - 0s - 39ms/step - loss: 0.0166 - mae: 0.0923 - val_loss: 0.0085 - val_mae: 0.0655\n",
      "Epoch 57/1000\n",
      "10/10 - 0s - 37ms/step - loss: 0.0162 - mae: 0.0914 - val_loss: 0.0098 - val_mae: 0.0753\n",
      "Epoch 58/1000\n",
      "10/10 - 0s - 36ms/step - loss: 0.0159 - mae: 0.0909 - val_loss: 0.0090 - val_mae: 0.0714\n",
      "Epoch 59/1000\n",
      "10/10 - 0s - 37ms/step - loss: 0.0156 - mae: 0.0898 - val_loss: 0.0079 - val_mae: 0.0638\n",
      "Epoch 60/1000\n",
      "10/10 - 0s - 37ms/step - loss: 0.0156 - mae: 0.0894 - val_loss: 0.0087 - val_mae: 0.0691\n",
      "Epoch 61/1000\n",
      "10/10 - 0s - 35ms/step - loss: 0.0152 - mae: 0.0892 - val_loss: 0.0085 - val_mae: 0.0660\n",
      "Epoch 62/1000\n",
      "10/10 - 0s - 38ms/step - loss: 0.0155 - mae: 0.0892 - val_loss: 0.0090 - val_mae: 0.0718\n",
      "Epoch 63/1000\n",
      "10/10 - 0s - 38ms/step - loss: 0.0159 - mae: 0.0897 - val_loss: 0.0112 - val_mae: 0.0828\n",
      "Epoch 64/1000\n",
      "10/10 - 0s - 37ms/step - loss: 0.0153 - mae: 0.0887 - val_loss: 0.0079 - val_mae: 0.0649\n",
      "Epoch 65/1000\n",
      "10/10 - 0s - 37ms/step - loss: 0.0154 - mae: 0.0893 - val_loss: 0.0092 - val_mae: 0.0731\n",
      "Epoch 66/1000\n",
      "10/10 - 0s - 39ms/step - loss: 0.0151 - mae: 0.0885 - val_loss: 0.0096 - val_mae: 0.0750\n",
      "Epoch 67/1000\n",
      "10/10 - 0s - 37ms/step - loss: 0.0147 - mae: 0.0867 - val_loss: 0.0090 - val_mae: 0.0701\n",
      "Epoch 68/1000\n",
      "10/10 - 0s - 38ms/step - loss: 0.0148 - mae: 0.0880 - val_loss: 0.0093 - val_mae: 0.0721\n",
      "Epoch 69/1000\n",
      "10/10 - 0s - 38ms/step - loss: 0.0151 - mae: 0.0883 - val_loss: 0.0086 - val_mae: 0.0688\n",
      "Epoch 70/1000\n",
      "10/10 - 0s - 39ms/step - loss: 0.0151 - mae: 0.0880 - val_loss: 0.0100 - val_mae: 0.0750\n",
      "Epoch 71/1000\n",
      "10/10 - 0s - 42ms/step - loss: 0.0147 - mae: 0.0869 - val_loss: 0.0093 - val_mae: 0.0727\n",
      "Epoch 72/1000\n",
      "10/10 - 0s - 37ms/step - loss: 0.0143 - mae: 0.0861 - val_loss: 0.0084 - val_mae: 0.0661\n",
      "Epoch 73/1000\n",
      "10/10 - 0s - 37ms/step - loss: 0.0143 - mae: 0.0863 - val_loss: 0.0084 - val_mae: 0.0681\n",
      "Epoch 74/1000\n",
      "10/10 - 0s - 39ms/step - loss: 0.0145 - mae: 0.0862 - val_loss: 0.0098 - val_mae: 0.0748\n",
      "Epoch 75/1000\n",
      "10/10 - 0s - 37ms/step - loss: 0.0143 - mae: 0.0858 - val_loss: 0.0083 - val_mae: 0.0672\n",
      "Epoch 76/1000\n",
      "10/10 - 0s - 37ms/step - loss: 0.0142 - mae: 0.0855 - val_loss: 0.0085 - val_mae: 0.0676\n",
      "Epoch 77/1000\n",
      "10/10 - 0s - 37ms/step - loss: 0.0138 - mae: 0.0845 - val_loss: 0.0083 - val_mae: 0.0661\n",
      "Epoch 78/1000\n",
      "10/10 - 0s - 38ms/step - loss: 0.0141 - mae: 0.0860 - val_loss: 0.0078 - val_mae: 0.0644\n",
      "Epoch 79/1000\n",
      "10/10 - 0s - 37ms/step - loss: 0.0140 - mae: 0.0851 - val_loss: 0.0082 - val_mae: 0.0668\n",
      "Epoch 80/1000\n",
      "10/10 - 0s - 36ms/step - loss: 0.0137 - mae: 0.0842 - val_loss: 0.0088 - val_mae: 0.0705\n",
      "Epoch 81/1000\n",
      "10/10 - 0s - 36ms/step - loss: 0.0135 - mae: 0.0834 - val_loss: 0.0084 - val_mae: 0.0685\n",
      "Epoch 82/1000\n",
      "10/10 - 0s - 39ms/step - loss: 0.0138 - mae: 0.0847 - val_loss: 0.0086 - val_mae: 0.0681\n",
      "Epoch 83/1000\n",
      "10/10 - 0s - 39ms/step - loss: 0.0137 - mae: 0.0844 - val_loss: 0.0097 - val_mae: 0.0759\n",
      "Epoch 84/1000\n",
      "10/10 - 0s - 39ms/step - loss: 0.0134 - mae: 0.0835 - val_loss: 0.0090 - val_mae: 0.0728\n",
      "Epoch 85/1000\n",
      "10/10 - 0s - 39ms/step - loss: 0.0133 - mae: 0.0831 - val_loss: 0.0095 - val_mae: 0.0756\n",
      "Epoch 86/1000\n",
      "10/10 - 0s - 36ms/step - loss: 0.0133 - mae: 0.0834 - val_loss: 0.0085 - val_mae: 0.0704\n",
      "Epoch 87/1000\n",
      "10/10 - 0s - 38ms/step - loss: 0.0132 - mae: 0.0825 - val_loss: 0.0086 - val_mae: 0.0714\n",
      "Epoch 88/1000\n",
      "10/10 - 0s - 38ms/step - loss: 0.0131 - mae: 0.0824 - val_loss: 0.0085 - val_mae: 0.0692\n",
      "Epoch 89/1000\n",
      "10/10 - 0s - 37ms/step - loss: 0.0131 - mae: 0.0822 - val_loss: 0.0079 - val_mae: 0.0662\n",
      "Epoch 90/1000\n",
      "10/10 - 0s - 39ms/step - loss: 0.0128 - mae: 0.0815 - val_loss: 0.0083 - val_mae: 0.0683\n",
      "Epoch 91/1000\n",
      "10/10 - 0s - 37ms/step - loss: 0.0129 - mae: 0.0820 - val_loss: 0.0094 - val_mae: 0.0733\n",
      "Epoch 92/1000\n",
      "10/10 - 0s - 38ms/step - loss: 0.0129 - mae: 0.0815 - val_loss: 0.0081 - val_mae: 0.0685\n",
      "Epoch 93/1000\n",
      "10/10 - 0s - 37ms/step - loss: 0.0126 - mae: 0.0809 - val_loss: 0.0090 - val_mae: 0.0710\n",
      "Epoch 94/1000\n",
      "10/10 - 0s - 38ms/step - loss: 0.0131 - mae: 0.0826 - val_loss: 0.0082 - val_mae: 0.0684\n",
      "Epoch 95/1000\n",
      "10/10 - 0s - 37ms/step - loss: 0.0129 - mae: 0.0813 - val_loss: 0.0083 - val_mae: 0.0688\n",
      "Epoch 96/1000\n",
      "10/10 - 0s - 38ms/step - loss: 0.0125 - mae: 0.0804 - val_loss: 0.0085 - val_mae: 0.0695\n",
      "Epoch 97/1000\n",
      "10/10 - 0s - 38ms/step - loss: 0.0128 - mae: 0.0812 - val_loss: 0.0076 - val_mae: 0.0638\n",
      "Epoch 98/1000\n",
      "10/10 - 0s - 40ms/step - loss: 0.0128 - mae: 0.0808 - val_loss: 0.0094 - val_mae: 0.0743\n",
      "Epoch 99/1000\n",
      "10/10 - 0s - 39ms/step - loss: 0.0125 - mae: 0.0802 - val_loss: 0.0084 - val_mae: 0.0689\n",
      "Epoch 100/1000\n",
      "10/10 - 0s - 38ms/step - loss: 0.0126 - mae: 0.0809 - val_loss: 0.0086 - val_mae: 0.0709\n",
      "Epoch 101/1000\n",
      "10/10 - 0s - 40ms/step - loss: 0.0129 - mae: 0.0813 - val_loss: 0.0086 - val_mae: 0.0706\n",
      "Epoch 102/1000\n",
      "10/10 - 0s - 38ms/step - loss: 0.0125 - mae: 0.0796 - val_loss: 0.0092 - val_mae: 0.0758\n",
      "Epoch 103/1000\n",
      "10/10 - 0s - 31ms/step - loss: 0.0123 - mae: 0.0804 - val_loss: 0.0099 - val_mae: 0.0779\n",
      "Epoch 104/1000\n",
      "10/10 - 0s - 35ms/step - loss: 0.0124 - mae: 0.0803 - val_loss: 0.0080 - val_mae: 0.0685\n",
      "Epoch 105/1000\n",
      "10/10 - 0s - 36ms/step - loss: 0.0122 - mae: 0.0792 - val_loss: 0.0091 - val_mae: 0.0737\n",
      "Epoch 106/1000\n",
      "10/10 - 0s - 32ms/step - loss: 0.0124 - mae: 0.0802 - val_loss: 0.0107 - val_mae: 0.0819\n",
      "Epoch 107/1000\n",
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      "Epoch 135/1000\n",
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      "Epoch 139/1000\n",
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      "Epoch 144/1000\n",
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      "Epoch 145/1000\n",
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      "Epoch 149/1000\n",
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      "Epoch 151/1000\n",
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      "Epoch 153/1000\n",
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      "Epoch 154/1000\n",
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      "Epoch 155/1000\n",
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      "Epoch 164/1000\n",
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      "10/10 - 0s - 35ms/step - loss: 0.0094 - mae: 0.0706 - val_loss: 0.0084 - val_mae: 0.0689\n",
      "Epoch 234/1000\n",
      "10/10 - 0s - 34ms/step - loss: 0.0095 - mae: 0.0704 - val_loss: 0.0090 - val_mae: 0.0739\n",
      "Epoch 235/1000\n",
      "10/10 - 0s - 38ms/step - loss: 0.0091 - mae: 0.0692 - val_loss: 0.0079 - val_mae: 0.0668\n",
      "Epoch 236/1000\n",
      "10/10 - 0s - 37ms/step - loss: 0.0091 - mae: 0.0690 - val_loss: 0.0084 - val_mae: 0.0688\n",
      "Epoch 237/1000\n",
      "10/10 - 0s - 37ms/step - loss: 0.0089 - mae: 0.0682 - val_loss: 0.0080 - val_mae: 0.0670\n",
      "Epoch 238/1000\n",
      "10/10 - 0s - 37ms/step - loss: 0.0091 - mae: 0.0692 - val_loss: 0.0083 - val_mae: 0.0700\n",
      "Epoch 239/1000\n",
      "10/10 - 0s - 38ms/step - loss: 0.0090 - mae: 0.0693 - val_loss: 0.0087 - val_mae: 0.0699\n",
      "Epoch 240/1000\n",
      "10/10 - 0s - 39ms/step - loss: 0.0090 - mae: 0.0687 - val_loss: 0.0089 - val_mae: 0.0697\n",
      "Epoch 241/1000\n",
      "10/10 - 0s - 36ms/step - loss: 0.0090 - mae: 0.0687 - val_loss: 0.0084 - val_mae: 0.0712\n",
      "Epoch 242/1000\n",
      "10/10 - 0s - 37ms/step - loss: 0.0091 - mae: 0.0691 - val_loss: 0.0079 - val_mae: 0.0670\n",
      "Epoch 243/1000\n",
      "10/10 - 0s - 39ms/step - loss: 0.0090 - mae: 0.0682 - val_loss: 0.0083 - val_mae: 0.0708\n",
      "Epoch 244/1000\n",
      "10/10 - 0s - 39ms/step - loss: 0.0091 - mae: 0.0692 - val_loss: 0.0079 - val_mae: 0.0658\n",
      "Epoch 245/1000\n",
      "10/10 - 0s - 38ms/step - loss: 0.0091 - mae: 0.0693 - val_loss: 0.0090 - val_mae: 0.0730\n",
      "Epoch 246/1000\n",
      "10/10 - 0s - 37ms/step - loss: 0.0090 - mae: 0.0692 - val_loss: 0.0086 - val_mae: 0.0705\n",
      "Epoch 247/1000\n",
      "10/10 - 0s - 39ms/step - loss: 0.0089 - mae: 0.0688 - val_loss: 0.0086 - val_mae: 0.0703\n",
      "Epoch 248/1000\n",
      "10/10 - 0s - 39ms/step - loss: 0.0091 - mae: 0.0692 - val_loss: 0.0078 - val_mae: 0.0675\n",
      "Epoch 249/1000\n",
      "10/10 - 0s - 39ms/step - loss: 0.0090 - mae: 0.0685 - val_loss: 0.0086 - val_mae: 0.0709\n",
      "Epoch 250/1000\n",
      "10/10 - 0s - 37ms/step - loss: 0.0086 - mae: 0.0676 - val_loss: 0.0077 - val_mae: 0.0655\n",
      "Epoch 251/1000\n",
      "10/10 - 0s - 39ms/step - loss: 0.0092 - mae: 0.0691 - val_loss: 0.0094 - val_mae: 0.0753\n",
      "Epoch 252/1000\n",
      "10/10 - 0s - 38ms/step - loss: 0.0091 - mae: 0.0692 - val_loss: 0.0092 - val_mae: 0.0733\n",
      "Epoch 253/1000\n",
      "10/10 - 0s - 38ms/step - loss: 0.0088 - mae: 0.0677 - val_loss: 0.0088 - val_mae: 0.0708\n",
      "Epoch 254/1000\n",
      "10/10 - 0s - 37ms/step - loss: 0.0092 - mae: 0.0695 - val_loss: 0.0092 - val_mae: 0.0744\n"
     ]
    }
   ],
   "source": [
    "cnn = build_cnn(X_trn, lr=0.0008, n_outputs=2*forward, dropout_rate=0.5)\n",
    "history = cnn.fit(X_trn, Y_trn, batch_size=32, epochs=1000, verbose=2, \n",
    "                  validation_data=(X_val, Y_val),\n",
    "                  callbacks=[EarlyStopping(monitor='val_mae', patience=200, restore_best_weights=True)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b0db7689-0616-4460-b488-c7be77c5b0bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zBxMnTkTfvn1x1113Vfs6/vjjD7Ro0QLbt2/Hrl27MGnSJOzZswf9+/fHvn37sHLlSkyePBl33XUXQkNDAQBubm5YsmQJgoODcezYMTz55JNwc3PDSy+9BADYuHEjxo0bh08++QT9+vXD2bNn8dRTTwEA3nzzzVp/tvXFILYxGTKxxiCWmVgiImruyouAd4KbZt+vXgLULrWu5u3tjaFDh2LZsmXGIPaHH36At7e38fuYmBjExMQYn/P222/jp59+wtq1a/Hss8/Wuo/vv/8eWq0WixcvhrOzMzp27IiLFy/i6aefNq6jUqkwe/Zs4/cRERHYvXs3Vq1ahYcffhiurq5wcnJCaWlpjeUDCxYsQGhoKD799FNIkoT27dvj0qVLePnll/HGG29AoRAX0jt37mwMEtu0aYNPP/0UW7ZsqTGI9fb2xieffAKFQoF27drhvffeQ1FREV599VUAwIwZM/Duu+9i165dGDNmDADgtddeMz4/PDwc//73v7Fy5UpjEPuf//wHr7zyijHrHRkZibfeegsvvfQSg9hbhiET68AgloiIqEE9+uijeOqpp7BgwQI4Ojri+++/x5gxY6BUKgGITOrs2bPx66+/4tKlS6ioqEBxcTFSU1PrtP2kpCTExMTA2dnZuCwuLq7Kel988QW+/vprXLhwAcXFxSgrK0OXLl1sei1JSUmIi4uDZJaF7tu3LwoKCnDx4kVj5rhz584WzwsKCkJWVlaN2+7YsaMxCAaAgIAAdOrUyfi9UqmEj4+PxXZ+/PFHzJ8/H2fOnEFBQQEqKirg7u5ufPzQoUM4cOAA/vOf/xiXabValJSUoKioyOJn1pAYxDYmQyZWqZUHdrFPLBERNXMqZ5ERbap919G9994LnU6H9evXo0ePHtixYwfmzZtnfPzFF1/Exo0b8cEHH6B169ZwcnLCqFGjUFZWVqft6/X6WtdZtWoVpk6dig8//BBxcXFwc3PD+++/j3379tX5dcj7kiqVUcj7N1+uUqks1pEkqUpdcGXWnlPTdvbu3YsxY8Zg9uzZGDJkCDw8PLBixQp8+OGHxvV1Oh1mz56NBx54oMr+NBpNjcdzIxjENiZDJlahKwUgMrE6nR4KRe31PkRERE1Ckup0Sb+pOTk54YEHHsD333+PM2fOoG3btoiNjTU+vmPHDkycOBH3338/AFEjm5KSUuftR0VF4X//+x+Ki4vh5OQEQAR45nbs2IE+ffrgmWeeMS47e/asxTpqtRpabc1JrKioKKxevdoimN29ezfc3NwQEhJS52NuCLt27UJYWBhmzpxpXHbhwgWLdbp164ZTp06hdevWjXps7E7QmAyZWIW21LioiFPPEhERNYhHH30U69evx+LFizFu3DiLx1q3bo01a9YgMTERR44cwdixY2vNWpobO3YsFAoFJk2ahBMnTmDDhg344IMPquzj4MGD2LhxI06fPo3XX38dBw4csFgnPDwcR48exalTp5CdnV1llD8APPPMM0hLS8Nzzz2HkydP4pdffsGbb76JadOmWZQCNIbWrVsjNTUVK1aswNmzZ/HJJ5/gp59+sljnjTfewNKlSzFr1iz89ddfSEpKwsqVKy1qaW+Gev0kFixYgIiICGg0GsTGxmLHjh3VrpuRkYGxY8eiXbt2UCgUmDJlSpV1Bg4caGw1Yf519913G9eZNWtWlcftrqeaIROLilIoDdlXdiggIiJqGHfccQe8vb1x6tQpjB071uKxjz76CF5eXujTpw/uvfdeDBkyBN26davztl1dXbFu3TqcOHECXbt2xcyZM/F///d/FutMnjwZDzzwAEaPHo1evXohJyfHIisLAE8++STatWuH7t27w8/PD7t27aqyr5CQEGzYsAH79+9HTEwMJk+ejEmTJt30oNCaESNGYOrUqXj22WfRpUsX7N69G6+//rrFOkOGDMGvv/6K+Ph49OjRA71798a8efMQFhZ2U49N0telyMPMypUrMX78eCxYsAB9+/bFwoUL8fXXX+PEiRNWW1SkpKTgo48+QmxsLD766CMMGDDAYko3ALh69apFTUpOTg5iYmLw9ddfY+LEiQBEEPvjjz9a9HRTKpXw8/Or87Hn5+fDw8MDeXl5FgXJjSYvHfgoClCoEKNfhrzicmyeNgCt/V0b/1iIiIisKCkpwfnz543JKqKboaa/s7rGazbXxM6bNw+TJk3CE088AQCYP38+Nm7ciM8//xxz586tsn54eLix0W/l5rkyb29vi+9XrFgBZ2dnPPTQQ5YH6+Bgf9lXc4ZyAujK4e4oIa+YHQqIiIiI6sOmcoKysjIcOnQIgwcPtlg+ePBg7N69u8EOatGiRRgzZgxcXCwLyZOTkxEcHIyIiAiMGTMG586dq3E7paWlyM/Pt/hqUnI5AQAvR5EA59SzRERERLazKYjNzs6GVqtFQECAxfKAgABkZmY2yAHt378fx48fN2Z6Zb169cLSpUuxceNGfPXVV8jMzESfPn2Qk5NT7bbmzp0LDw8P45c880STcTClyz3Vopj8OmtiiYiIiGxWr4Fd1nqXVV5WX4sWLUKnTp3Qs2dPi+XDhg3Dgw8+iOjoaAwaNAjr168HIKZcq86MGTOQl5dn/EpLS2uQY6w3pQMgiabLchDLTCwRERGR7WyqifX19YVSqaySdc3KyqqSna2PoqIirFixAnPmzKl1XRcXF0RHRyM5ObnadRwdHeHo6Fjt403CQQOUF8JDrQMgsSaWiIiIqB5sysSq1WrExsYiPj7eYnl8fDz69OlzwwezatUqlJaWVuntZk1paSmSkpIQFBR0w/ttVIa6WA8H0R+WQSwRETVHtvRQJbJVQ/x92dydYNq0aRg/fjy6d++OuLg4fPnll0hNTcXkyZMBiEv46enpWLp0qfE5iYmJAMTsGFeuXEFiYiLUajWioqIstr1o0SKMHDkSPj4+VfY7ffp03HvvvWjZsiWysrLw9ttvIz8/H4899pitL6FpGepi3VRaAA4MYomIqFlRq9VQKBS4dOkS/Pz8oFarG6xkkEiv16OsrAxXrlyBQqGAWq2u97ZsDmJHjx6NnJwczJkzBxkZGejUqRM2bNhgbGibkZGB1NRUi+d07drVeP/QoUNYtmwZwsLCLKZ7O336NHbu3IlNmzZZ3e/FixfxyCOPIDs7G35+fujduzf27t170xvpNjhDJtbdkIllTSwRETUnCoUCERERyMjIwKVLl5r6cOgW5ezsjJYtW97QDGQ2T3Zgz5p8sgMA+Kw3cCUJ67osxHN73fBA1xDMG92laY6FiIioGnq9HhUVFdBqOT06NSylUgkHB4dqM/w3bbIDukGGTKyrg8jAspyAiIiaI0mSoFKpoFKpmvpQiKyqfw6X6sdQE+ui4MAuIiIiovpiENvYDJlYZ4UIXlkTS0RERGQ7BrGNzZCJdVKUAwCuM4glIiIishmD2MbmIFpJOEnMxBIRERHVF4PYxmbIxGokkYktKGEQS0RERGQrBrGNzVAT62gIYgvLtNDp/jZdzoiIiIgaBIPYxmbIxDrqy4yLCsuYjSUiIiKyBYPYxmYIYpW6MjgoRJPfwlI2kiYiIiKyBYPYxmYoJ5C0pXDViLkmCkrLm/KIiIiIiOwOg9jGZsjEoqIELmo5iGUmloiIiMgWDGIbmyETi4pSuMmZWHYoICIiIrIJg9jGZp6JdWQ5AREREVF9MIhtbGaZWFdHlhMQERER1QeD2MZmlok1BrElzMQSERER2YJBbGOzkoktLGMmloiIiMgWDGIbm5Wa2Osc2EVERERkEwaxjc08E2voTlBYyiCWiIiIyBYMYhubMRNbCldHJQCggEEsERERkU0YxDY2i5pYFQAGsURERES2YhDb2My7ExjKCfKL2Z2AiIiIyBYMYhubWTmBt7MaAJBbxCCWiIiIyBYMYhubsZygBF4uopwgp7CsCQ+IiIiIyP4wiG1sciZWWwofQyb2WlEZ9Hp9Ex4UERERkX1hENvY5EwsAE9HHQBAq9Mjn71iiYiIiOqMQWxjkzOxADQoh4tatNm6xpICIiIiojpjENvYFA6AZPixV5TC21WUFLAuloiIiKjuGMQ2NkkClKbBXXKHAmZiiYiIiOqOQWxTMJvwwMtFBLFXixjEEhEREdUVg9imYDbhATOxRERERLZjENsUzDKx3nImlkEsERERUZ0xiG0KZplYLwaxRERERDZjENsUrGRir7EmloiIiKjOGMQ2BfNMrDNbbBERERHZikFsU3AwtdjyceXALiIiIiJbMYhtCsZMbKkxE8uaWCIiIqK6YxDbFMwysXJNbH5JBcq1uiY8KCIiIiL7Ua8gdsGCBYiIiIBGo0FsbCx27NhR7boZGRkYO3Ys2rVrB4VCgSlTplRZZ8mSJZAkqcpXSUlJvffbrJllYj2cVFBI4lsO7iIiIiKqG5uD2JUrV2LKlCmYOXMmEhIS0K9fPwwbNgypqalW1y8tLYWfnx9mzpyJmJiYarfr7u6OjIwMiy+NRlPv/TZrZgO7lAoJnsYJD8qb8KCIiIiI7IfNQey8efMwadIkPPHEE+jQoQPmz5+P0NBQfP7551bXDw8Px8cff4wJEybAw8Oj2u1KkoTAwECLrxvZb7NmVk4AAF7OKgCsiyUiIiKqK5uC2LKyMhw6dAiDBw+2WD548GDs3r37hg6koKAAYWFhaNGiBe655x4kJCQ0yn6bhMpJ3JYXAwB7xRIRERHZyKYgNjs7G1qtFgEBARbLAwICkJmZWe+DaN++PZYsWYK1a9di+fLl0Gg06Nu3L5KTk29ov6WlpcjPz7f4ahZUzuK2UhDLXrFEREREdVOvgV2SJFl8r9frqyyzRe/evTFu3DjExMSgX79+WLVqFdq2bYv//ve/N7TfuXPnwsPDw/gVGhpa72NsUGo5iC0CYJaJZRBLREREVCc2BbG+vr5QKpVVsp9ZWVlVsqQ3dFAKBXr06GHMxNZ3vzNmzEBeXp7xKy0trcGO8YaoLINY9oolIiIiso1NQaxarUZsbCzi4+MtlsfHx6NPnz4NdlB6vR6JiYkICgq6of06OjrC3d3d4qtZkGtiyywzsQxiiYiIiOrGwdYnTJs2DePHj0f37t0RFxeHL7/8EqmpqZg8eTIAkf1MT0/H0qVLjc9JTEwEIAZvXblyBYmJiVCr1YiKigIAzJ49G71790abNm2Qn5+PTz75BImJifjss8/qvF+7oqqmnIADu4iIiIjqxOYgdvTo0cjJycGcOXOQkZGBTp06YcOGDQgLCwMgJjeo3Lu1a9euxvuHDh3CsmXLEBYWhpSUFABAbm4unnrqKWRmZsLDwwNdu3bF9u3b0bNnzzrv165UGtjlxUwsERERkU0kvV6vb+qDaCz5+fnw8PBAXl5e05YWnNsKLB0B+EcBz+zBkbRcjPhsF4I9NNg9486mOy4iIiKiJlbXeK1e3QnoBlVTTpBdWIa/0TkFERERUb0xiG0KchBrGNjl7y5m8Cqr0OFaEaeeJSIiIqoNg9imUGnGLkcHJXxdRSB7Kbe4qY6KiIiIyG4wiG0KxnKCQsBQPhDkoQEAZOaVNNVREREREdkNBrFNQZ6xS68DtKIjgRzEZuQxE0tERERUGwaxTUHOxALGwV2mIJaZWCIiIqLaMIhtCkoVoFCJ+4bBXUGeok6WQSwRERFR7RjENpVKEx7ImVgO7CIiIiKqHYPYpmLsUFAIAAjyEN9n5jMTS0RERFQbBrFNRW09E5uRV8IJD4iIiIhqwSC2qRgnPBCZ2AB3EcSWVehwtbCsqY6KiIiIyC4wiG0qlWpi1Q4K44QHHNxFREREVDMGsU2l0qxdABDsyTZbRERERHXBILapqF3ErWFgFwAEunPCAyIiIqK6YBDbVKxmYtkrloiIiKguGMQ2FTmILTPLxModCtgrloiIiKhGDGKbikouJzAFrJx6loiIiKhuGMQ2FWM5QZFxkTzhAYNYIiIiopoxiG0qxoFd5kGsyMRm5pVAp+OEB0RERETVYRDbVKwM7Apw10CSgDKtDleLOOEBERERUXUYxDaVSjN2AWLCAz/DhAdpV4usPYuIiIiIwCC26VSasUvW2t8VAJCcVdDYR0RERERkNxjENhUrA7sAoG2AGwDgdOb1xj4iIiIiIrvBILapWBnYBQDtAkUQe+oyg1giIiKi6jCIbSrGyQ6sZ2KTL7OcgIiIiKg6DGKbSjU1sW0DRE1sZn4J8orKG/uoiIiIiOwCg9imYgxiLTOxbhoVgg39Yk9nsaSAiIiIyBoGsU1FbT2IBYC2cl0sB3cRERERWcUgtqnImdiKEkCns3ionbEulkEsERERkTUMYpuKPLALqLbNFjsUEBEREVnHILapOJgHsZaDu9qZlRPo9frGPCoiIiIiu8AgtqkoFKZAtrzQ4qHW/q6QJOBaUTmyC8qa4OCIiIiImjcGsU1Jbb3NlkalRLiPmAzhNEsKiIiIiKpgENuUqmmzBZgGd+0/f7Uxj4iIiIjILjCIbUrVzNoFAEM6BQAA1iRchE7HulgiIiIicwxim1I1s3YBwNCOQXB1dEDa1WIcSGE2loiIiMgcg9imZAxiC6s85KRW4p7OQQCAHw5dbMyjIiIiImr26hXELliwABEREdBoNIiNjcWOHTuqXTcjIwNjx45Fu3btoFAoMGXKlCrrfPXVV+jXrx+8vLzg5eWFQYMGYf/+/RbrzJo1C5IkWXwFBgbW5/Cbj2oGdslGxbYAAGw4loHC0gqWFRAREREZ2BzErly5ElOmTMHMmTORkJCAfv36YdiwYUhNTbW6fmlpKfz8/DBz5kzExMRYXWfr1q145JFH8Oeff2LPnj1o2bIlBg8ejPT0dIv1OnbsiIyMDOPXsWPHbD385sVYE1s1EwsAsWFeCPdxRlGZFoM/2o52r/+GaSsTG+/4iIiIiJopm4PYefPmYdKkSXjiiSfQoUMHzJ8/H6Ghofj888+trh8eHo6PP/4YEyZMgIeHh9V1vv/+ezzzzDPo0qUL2rdvj6+++go6nQ5btmyxWM/BwQGBgYHGLz8/P1sPv3lRiTZa1WViJUnCwz1CAQDpucUo1+rxy5FLKCitaKwjJCIiImqWbApiy8rKcOjQIQwePNhi+eDBg7F79+4GO6iioiKUl5fD29vbYnlycjKCg4MRERGBMWPG4Ny5cw22zyYhZ2KrCWIB4Ml+kfjgoRgseqw7QjydoNXpOdCLiIiI/vZsCmKzs7Oh1WoREBBgsTwgIACZmZkNdlCvvPIKQkJCMGjQIOOyXr16YenSpdi4cSO++uorZGZmok+fPsjJyal2O6WlpcjPz7f4alZqGNhlXEWpwKjYFrizQwD6tvYBAOw9W/1rJiIiIvo7qNfALkmSLL7X6/VVltXXe++9h+XLl2PNmjXQaDTG5cOGDcODDz6I6OhoDBo0COvXrwcAfPvtt9Vua+7cufDw8DB+hYaGNsgxNphaBnZVFtdKBLF7zjGIJSIior83m4JYX19fKJXKKlnXrKysKtnZ+vjggw/wzjvvYNOmTejcuXON67q4uCA6OhrJycnVrjNjxgzk5eUZv9LS0m74GBtUDZMdWNM7UgSxx9PzkF9SfrOOioiIiKjZsymIVavViI2NRXx8vMXy+Ph49OnT54YO5P3338dbb72F33//Hd27d691/dLSUiQlJSEoKKjadRwdHeHu7m7x1awYB3bVLYgN8nBCuI8zdHrgAKejJSIior8xm8sJpk2bhq+//hqLFy9GUlISpk6ditTUVEyePBmAyH5OmDDB4jmJiYlITExEQUEBrly5gsTERJw4ccL4+HvvvYfXXnsNixcvRnh4ODIzM5GZmYmCggLjOtOnT8e2bdtw/vx57Nu3D6NGjUJ+fj4ee+yx+r72pmcc2FW3IBYwKylgXSwRERH9jTnY+oTRo0cjJycHc+bMQUZGBjp16oQNGzYgLCwMgJjcoHLP2K5duxrvHzp0CMuWLUNYWBhSUlIAiMkTysrKMGrUKIvnvfnmm5g1axYA4OLFi3jkkUeQnZ0NPz8/9O7dG3v37jXu1y6pDZnYavrEWtM70gfL96exLpaIiIj+1iS9Xv+3mQYqPz8fHh4eyMvLax6lBac3AcseAoJigH9ur9NTsvJL0POdLZAkYM8rdyLQQ1P7k4iIiIjsRF3jtXp1J6AGojH8Ykry6vwUf3cNekZ4Q68HFm4/e5MOjIiIiKh5YxDblBzlINa2/rXP3dEaALBsXyqyrpc09FERERERNXsMYpuSnIktzQdsqOq4rbUvuoR6orRCh693nL9JB0dERETUfDGIbUpyJlZXUecJDwAx2cTzd4ps7P/2XMAnW5Lx56ksVGh1N+MoiYiIiJodBrFNSe0KwDDTWaltJQW3t/NHTKgnisu1mBd/Go9/cwCf/HGmynpZ10twPL3uNbdERERE9oBBbFNSKMwGd9kWxEqShP9N6onX7u6AQR3EbGnL96eivFI29qmlh3Dfpzvx1yUGskRERHTrYBDb1Bw9xK2NmVgAcNeo8ES/SCx4tBt8XdW4cr0Uf57MMj6edb0EiWm50OmBraeuNNQRExERETU5BrFNrR5ttipTOyjwYLcWAICVB9KMy/eeu2p2n5MjEBER0a2DQWxTczTrUHADHu4RCgD481QWMvNE2y3zqWkPplyrUmpAREREZK8YxDa1BsjEAkArP1f0jPCGTg/8cFBkY/eZZV+Ly7U4epF1sURERHRrYBDb1Oo54YE1YwzZ2G/3pODslQKcyy6EQgL6tvYBwJICIiIiunUwiG1qmoYpJwCAezoHI8LXBdkFZXjmu8MAgE4hHrjL0L2AQSwRERHdKhjENrUGzMSqHRR4eWh7AMCpy9cBAHGRPujdSmRiD6Zcw5Xrpfgp4SKy8jldLREREdkvh6Y+gL+9BszEAsCQjgHoGe6N/SmiM0HvVj5o6+8GL2cVrhWVo8+7W1Cu1SPYQ4NlT/ZGuK9Lg+yXiIiIqDExE9vUGjATC4hJEF69u4PYtIMCPcK9oVBIiDNkY8u1ejg6KHAprwRjvtyL89mFDbJfIiIiosbETGxT09R/soPqdAn1xLf/6AmNgwKujuJX/MrQDgj1dsaANn5oHeCKR7/ah+SsAoz7eh/WPXcbvF3UVbaTnluMKSsScHt7fzwzsHWDHR8RERHRjWImtqnJQewNttiqbEBbP/SK9DF+39LHGTOGdUCf1r7wd9Ng+VO9Ee7jjPTcYrywIgFand7i+SXlWkz+3yEcSLmGBX+eZY9ZIiIialYYxDa1BprswFa+ro74YnwsNCoFdiRn44NNp6AzBLJ6vR6v/Xwcx9JFYF1QWoEjabmNenxERERENWE5QVPTNGxNrC3aB7rj3Qc6Y8rKRHy+9Sw2/ZWJOzsEYGdyNk5k5EMhiUkUkrMKsCM5G93DvRv9GImIiIisYSa2qTmazdil19e87k0wsmsIXh3eHm6ODjh7pRBfbj+HExn5UCokzLqvIybdFgEA2Hkmu9GPjYiIiKg6zMQ2NTkTq9cC5UWAuvFbXj3VvxUe6dkSqw5exF/peegV6Y27ogLh7aJG2tUiAEBiWi6ul5TDTaNq9OMjIiIiqoxBbFNTuwKSAtDrRElBEwSxAOCmURmzruZCvZ0R7uOMlJwi7D13FXdFBTTB0RERERFZYjlBU5MkwNFN3G+owV16PbBuCrDp9QbZXN/WvgCAXSwpICIiomaCQWxz4Ci32WqgILYwGzj0DbD7E6Ci7IY316+NCGK3J1+BvgnqdomIiIgqYxDbHBgnPGigXrFl1033y4tueHNxkb5QKSWcu1KI2etOGFtxERERETUVBrHNQUO32SozC1zLi294cx7OKsy+rxMAYMnuFEz/8QhOX77OrCwRERE1GQ7sag4aesID8+xrA2RiAWBsr5ZQOyjw0o9HsOZwOtYcTkeAuyNeuzsK98YEN8g+iIiIiOqKmdjmoMEzsYWm+7YEsRVlwNVz1T48KrYFFk/sgX5tfOHooMDl/FI8tzwB7288yRIDIiIialTMxDYH5hMeNATzwLXMhiD2t5fEgLDH1gER/a2uMrCdPwa280dJuRYfxZ/Gwu3n8NmfZ7Fifxqigt0R4K6BSikh0N0Jj/ZuCV9Xxxt8MURERERVMYhtDjQNXE5QVs9ygtQ94jbjaLVBrEyjUmLG8A5oF+iG134+jpzCMuxItmzB9cW2s5gQF4bn72wDF0f+qREREVHDYWTRHDg2cDlBeT3KCXRaUylBYVadd/VAtxYYHh2EU5nXcSIjH7lF5ajQ6rA56TKOXMzDwu3nsO30FXw1oTtCvZ1teBFERERE1WNNbHNQUyY2/RDwYXsgcXndt1ef7gS5FwCtoadsQd2DWEBkZWNCPfFIz5Z4emArPHdnG/z8r75Y9Fh3+Lo64mTmdYz4bBf2ncuxabtERERE1WEQ2xzUlIk98wdwPQM4tb7u2zPPxJoP8qpJ9hnT/YLLdd9XNSRJwp0dArDuub7oFOKOq4VlePTrfVi+PxUl5VrsPZeDxTvP47Wfj+GV1UeRV1R+w/skIiKivw+WEzQHNU12UGSoMy3Orfv26pOJzUk23S+4Uvd91SLIwwk//LMPpv94BOuPZmDGmmN445fjKNdadjMoKddi/piuDbZfIiIiurUxE9scaGqYdrawHkGsRZ/YumZiT5vuN0Am1pyTWolPH+mKf9/VVhySVg8/N0cM6RiAx/uGQyEBPydewh8nG3a/REREdOtiJrY5qGmyAzkTW5Jb9+1Z9ImtYybWvJygKFsM9FIo677PWkiShOfubIMHY1ugpFyLCF8XSJIEAHBQSPhqx3m8uuY4Nk3zhrtG1WD7JSIiolsTM7HNgbOPuC2+BlSUWj5WaBgMZVM5gXlNbB27E5iXE+h1pgxwAwv2dEKkn6sxgAWAaXe1Q7iPMzLzSzBv0+kqz7mUW4zdZ2/O8RAREZF9YhDbHLj4Ag5O4n7eRcvH5Exs2XVAW8fBT7ZOO1uSbyohULuKWxvabN0oJ7US/7k/GgDwv70XcCarwPjYtcIy3L9gF8Z+tQ8HUq422jERERFR81avIHbBggWIiIiARqNBbGwsduzYUe26GRkZGDt2LNq1aweFQoEpU6ZYXW/16tWIioqCo6MjoqKi8NNPP93Qfu2KJAGeoeJ+XpppuV5vmRGt64xetk47K2dhXfwBrwhxv4HrYmvTt7UvBnXwh1anx7u/JQEA9Ho9Xl59FJfzRXZ63ZFLjXpMRERE1HzZHMSuXLkSU6ZMwcyZM5GQkIB+/fph2LBhSE1Ntbp+aWkp/Pz8MHPmTMTExFhdZ8+ePRg9ejTGjx+PI0eOYPz48Xj44Yexb9++eu/X7ngYgthcsyC2NB/QmWVf61pSYGsmVq6H9W0DuPqJ+zb2im0IM4Z3gINCwuakLCzZdR7zNydj0wlTMP378UzodPoatkBERER/FzYHsfPmzcOkSZPwxBNPoEOHDpg/fz5CQ0Px+eefW10/PDwcH3/8MSZMmAAPDw+r68yfPx933XUXZsyYgfbt22PGjBm48847MX/+/Hrv1+7Imdhcs6C8cl1qXQd3mdfB1qUmVu5M4NsGcA0Q95sgiG3l54pxvcMAALPWncDHW0SG+MUh7eDm6ICs66U4nHqt0Y+LiIiImh+bgtiysjIcOnQIgwcPtlg+ePBg7N69u94HsWfPnirbHDJkiHGb9d1vaWkp8vPzLb6aLc+W4ta8nKCo0gxXxXUM4Mpt7E4glxP4tAFc/cX9JghiAWDqoLYYHh2Iri090SnEHeN7h+HpAa1wZwdxXL8dz2yS4yIiIqLmxaYWW9nZ2dBqtQgICLBYHhAQgMzM+gcXmZmZNW6zvvudO3cuZs+eXe/jalQehiDWvJygcia2ruUEZTb2iTUvJ5A1ck2szMNZhQWPxlZZPiw6CD8nXsLvxzPx0tB2OJ6eD383R7TwcrLodEBERER/D/XqE1s5aNDr9TccSNRlm7bud8aMGZg2bZrx+/z8fISGht7Qcd401soJiupZTlBu44xduRfErXekacKFJgpiqzOgrR+c1Uqk5xYj9q3NKCitAAD4uzliVGwLTB/cDgoFg1kiIqK/C5vKCXx9faFUKqtkP7OysqpkSW0RGBhY4zbru19HR0e4u7tbfDVbcjlBfjqgFQFavTKxOp1lEFtbTWxFKVBmaGnl4msqJyhsuKlnG4JGpcQd7cWxFZRWwNNZBQeFhKzrpViw9SxeWn0UWg76IiIi+tuwKYhVq9WIjY1FfHy8xfL4+Hj06dOn3gcRFxdXZZubNm0ybvNm7bdZcQ0EFCpArwWuZ4hl9amJraiUea2tO0GRofeqpAAcPcwGdjWvTCwAvHFPFF4Z1h4rn+qNQ6/dheOzh+D/HoyGUiHhx0MX8cz3h3Ag5SqDWSIior8Bm8sJpk2bhvHjx6N79+6Ii4vDl19+idTUVEyePBmAuISfnp6OpUuXGp+TmJgIACgoKMCVK1eQmJgItVqNqKgoAMALL7yA/v374//+7/8wYsQI/PLLL9i8eTN27txZ5/3aPYUC8GgBXDsvSgo8Q02ZWI2nKCWoSzlB5cxrrUGsIVB28hLHIGdii68BFWWAg9qGF3Fz+btrMHlAK+P3SoUSo3u0hKujCi+sSMDGvy5j41+X4e2ixu3t/HFXlD/u7BAAlZJzehAREd1qbA5iR48ejZycHMyZMwcZGRno1KkTNmzYgLAw0RopIyOjSu/Wrl27Gu8fOnQIy5YtQ1hYGFJSUgAAffr0wYoVK/Daa6/h9ddfR6tWrbBy5Ur06tWrzvu9JXiGiiBW7lAg18T6tgEuHqhbOUHlgVwVJaLEQFFNIFdsyMTKU99qPEVGWFcuSgo8Qmx9FY3u7s5B8Hd3xPd7L+CPk1m4WliG1YcvYvXhi+gV4Y0vJ3SHh5OqqQ+TiIiIGlC9BnY988wzeOaZZ6w+tmTJkirL9PraL++OGjUKo0aNqvd+bwmVOxTImVif1oYgtg7lBHImVuViCmjLiwBHV+vry+UETt7iVs7G5qeLkgI7CGIBoEe4N3qEe6Ncq8PBlGvYknQZKw6kYd/5q3joi9345vGeCPF0aurDJCIiogbC66zNiTy4S+4WIF/q9zFcQpfLCa6lAFlJ1rchlw84e5stq6FDgTETa7a+S9PN2nWjVEoF4lr54LV7ovDD5DgEuDvi9OUC3DVvGz7cdAr5JaYZ0H5JTMf9C3bhj5PNr/6XiIiIasYgtjmR22zlVc7EGvq3FueK0oBFg4EvbwdK8qpuo8yQfVW7ACpncb+mXrHGmlizILYZD+6yRYcgd6x5pi+6hHqiqEyL//5xBv3+7098se0sPtmSjBdWJCIhNReT/3cYO5KvQKvT40DKVaRk16G3LhERETWpepUT0E3iIfeKTRPBqNxpwKe1uC2+JgJcObi8eh4I7mK5DTmIVTmLr/KimjOxRYYSBWcv0zJjmy37y8RWFuLphJ+e6YONf13Gh5tOITmrAO/+dtL4eKSfC85dKcRTSw/By1mFS3klcHN0wJZ/D4C/u6YJj5yIiIhqwkxsc2KcevaiqU+rg8ZUl1pRDFz+y7R+fnrVbcjlBOaZ2Jp6xVYe2AWYMrHXb40pXiVJwtBOgfh9Sn988FAMQjydoFRImDOiI357oR/6t/VDcbkWl/JKAADXSyvwf7+fauKjJiIiopowE9ucuAeLfq3aUlPNq7Ov6N8KCYAeuLjftH7+parbsCgnMAxkqqnNVuWBXYBp+tmMo/V5Fc2WUiFhVGwLjOwSbJgwQbQPWzguFt/uSUGIpxP83Bwx5su9WH34Isb1bokuoZ44n12IP09dweEL1zCmZyj6tfFr4ldCREREDGKbE6VKTP2acwZIXCaWufiIjgEaDzGwK+2Aaf28i1W3IQesKmdA7Wy5zBprA7tCe4rbjESgvARQ3VqX1R2UCmMACwBOaqVF/9lRsS3w46GLePq7w9BDj8v5pcbHNiddxtJ/9ESvSB8QERFR02EQ29x0ewyIfx1IWiu+d/YVt06eIohNP2Ra11o5gTET62w2sKumTKyVgV1eEaJDQeEVIOMI0LKX9efeol4a2g6/H89EZr4oL1ApJfSK8EGZVof956/iiW8PYnSPUOw8k43rJRWICnZHj3AvjO8dDie1somPnoiI6O+BQWxzEzsR2P4+UJovvneRg1gv0VrLfFpZa+UE5WZ9YutSE1tkpSZWkoDQXsDJX4G0vX+7INbfTYNv/9EDB1KuIaaFJ7qEesJJrURJuRbjF+3DgZRr+HrneeP66bnFiD9xGWuPXMLC8d3Zj5aIiKgRcGBXc6NxB2IfM30vZ2I1nlXXtVZOIAesaufaa2J1WlObLvNyAsBUUpC2Hw2m9DrwaU/g16kNt82bJDbMG5MHtEJcKx9jdlWjUuLrx3rgns5BGNElGB+P6YIVT/XGzOEd4O2ixvH0fNz33534KeEiyrW6Jn4FREREtzYGsc1Rr6cBhSFJ7mLIkDp5mh5XGKZQzb8k+saak3vCqlzE4C6g+iC2OBeAYTY1Jy/Lx0J7i9u0fUAdZlyrk0uJQPYp4PBSU9mDnfFwUuHTsd3w8ZiuGNElBL0jffBk/0isfbYvooLckVNYhqkrj2Dg+1vx61ErmXIiIiJqEAximyOPECD2cXE/JFbcmmdiW3QHIAG6cqAo2/K5VjOx1fSJlethHd3FoDJzQTGAUi3qYq+dr/rc+pD72+oqRHB8C2nh5YzVT/fBi0PawddVjfTcYjy7LAGfbz1bp2mXiYiIyDYMYpurYe8BU44DkQPF9+aZUv8owC1Q3K9cUmDencBYE1tN1tNaZwKZSgMEdRH3G6qkwHwa25RdDbPNZsRJrcS/bm+NnS/fgUm3RQAA/u/3k/j3D0dwPD3PajBbUq7F78czcPRibiMfLRERkX3jwK7mSqEwTUMLWJYT+LYVXQOuZ4gOBSHdTI9ZnXa2ukyslR6x5kJ7ir60afuAmDGWj53eBGx/Dxj+PhDctW6vyXwa2wu3XhAr06iUeP2eKAR5aPD2+iSsOZyONYfTEe7jjJhQT7T2c0W5To+s/BL8djwTecXlcFBImPtANEbFtkD8ics4kHIVE/tGcJAYERFRNRjE2gvzcgLf1mJihHRU7VBgS5/YmjKxgAhi9wC4eMBy+ZVTwI+PA2UFwLEfbQhizTKx6YdEcK26dYO0J/pFomOwB77bdwHxJy4jJacIKTlVfxduGgdcL6nAiz8exefbzuLcFXEisvJAGt55IBr3dA5u7EMnIiJq9hjE2gvzcgLftoBHC3G/cjlBmZVpZ6sLYmvLxPpHidur58XgLkkCSguAleNFAAuYpsetC/NMrLZMBMcR/ev+fDsU18oHca18kF9SjkMp13AiIx/nswuhUSng6qhC70hv3NbaFx/Gn8bnW0UA66xWooWXE05fLsCzyxKQerUIzwxsDQDIKyrH1tNZOJByFVn5peja0gt9W/sgOsQDkiQ18aslIiJqPAxi7YVcTuDgBLi3ANxDxPeVJzwot1JOUF2fWHlgl3M1s095tgQgiYC1MBtw9QM2vSY6DMjqE8Q6+4oBaSk7b/kgVuauUeH29v64vb2/1cdfHtoe7QPdcO5KIcb1DoOnswofxZ/Ggq1nMW/TaQxo6wcXtQNGfbEH2QWmGcQ2nRA/00d6tsR/RnaCQsFAloiI/h4YxNqLgE6Ai78Y6KVQiHICAMirFMSWmQ/sqqVPbG3lBA6OIljOvygmWnD1A5LjxWM9ngQOfAUU1COIjRoBHFx0Sw7uuhEjuoRYfP/ikHY4n12I345n4t+rjqC4XIvsglK08HLC4KhABHo44kDKNWxJuozl+1Oh1+vxzv3RDGSJiOhvgUGsvXD2BqYlAUrDr0wuJ6iuJlbtXHufWGM5gZf1xwHAK9wQxJ4H/DuI+wDQ/m4RxNY1E6utENlcAOj0gAhiLx4AKkpFsExVSJKEt0Z2wt5zOTiZeR0A0MLLCWue6QN/Nw0A4Kn+wC+J6Zi6MhErDqThamEZ3hrZCQHumqY8dCIiopuOQaw9UZr9uuRyguuXxMxbCqWoWy0zm+ygtj6xxdfEbXWZWADwDgcu7BSZ2JwzhvV9Ab924n7hFTHhgqKWbm1F2QD0gKQAWsaJ4ysvFDW9Pq1qfu7fmK+rI+aM6ITnlifAy1mFb//R0xjAyuQM7rRVR7DpxGXsOZuD3q18cCGnENdLKuDjqkaQhxMGRwVgeHQQXByr/tuXlGuRfLkAnULcWVtLRER2gUGsvXINEAGhrkKM+ncPMgSrhl6kamcRKALV94mtrSYWEJlYQAzuyk4W933bmqbD1WuBktyaA2HAVErg4icCbicvEcQW59b8PMK9McEI9nRCCy+najOsI7qEoLW/K1796TiOpOUi/oRpEF1GXgmOp+cj/sRlzFr7F/q29kXvSB8Miw5EkIcTSiu0GP3lXhxJy8WwToF4b1RnuGlUVvdDRETUXDCItVdKB8AtSAzsyr9kCGLNygZUdZmxq5buBADgJZr241qKaUCXX1vAQS3afpXkiiC61iDW0F7L1TCwyclLlCaUXKv5eQQAiA2roeTDoGOwB9Y83QcbjmUg63opIv1c4OWsRk5BKU5cysfqwxeRklOETScuY9OJy/ho82l8MS4Wvx3PwJG0XADAb8czcTLzOhY82g0dgtxv8qsiIiKqPwax9syzpQhiz24BWsSaMq4OGpHtrKlPrF5f+8AuwCyIPS8GdgEiEwuIrGpJrqEutn3NxypnYl0DxK3cbYGZ2AalVEi4N6ZqX9k7OwTg2Tta4+jFPOw5l4O1iZdwIiMf4xftg87QPe2Voe2xdM8FnM8uxMjPduGtkZ3wcHfThBsrD6Tij5NZ6BHujf5t/dA2wK0xXxoREZEFTjtrz2InitvtHwDZZywnOjC/LS8SQau50uuiFAGoORPrbQhir2cAGUfFfTmIlbOqhVlVn1eZMYg1TJdrDGKZiW0skiQhJtQTkwe0wppn+mBEl2DoDH8Wz93eGv8c0Aq/PncbBrbzQ2mFDi/9eBT/XnUE10vKsfJAKl5efQwb/7qMt9cnYfBH2zFtVSKKyiqa9kUREdHfFjOx9qzzaODoSuDsH8C6F4C7ZovlclcCOYjV60QXAJVZPaVcD+vgZMrYWuPkBTi6A6X5IhsLAL5txK2LoS5W7jpQk+tyEGtWTgAwiG0iGpUS80d3Qc8Ib1wtKMMzt4vJFLxc1Fj8WA8s2HoG8+JPY/Xhi9h9NhuX80sAAHdHB+F6aQV2Jl/BmsPpOJKWi5l3d0CfVr5wUEg4dfk6XNQOCPd1acqXR0REfwMMYu2ZJAH3fAQsiBMdBNY8JZZXzsQCIhtrHsTKU8l6R9a+D69wINOQhXXQAB4txX0XQ0BaYEsmVi4nkIPY3Nqfm3MWyEoCOtxT+7r2qLwEUKpECUgjkiQJj/YKq7JcoZDw7B1t0CvSB1NXJuLiNVFTPaZHKOY+EA1JkrD3XA5eWJGAs1cK8Y8lB+GkEsdeXK4FAPxzQCReHNwODkpe7CEiopuDnzD2ziscGPIOAAm4elYs03iIW6UDoFSL+5XrYk//Lm7bDq59H3JJAQD4tDG103Ix1MjWpVestYFdQN0ysaufAFY+ClxKqH1de1NaAHzcGfj2vqY+kip6hHvjtxf64an+kXh6YCv85/5oY/ut3pE+2PB8P0yIC0OQhwbF5VoUl2vhohbB7MJt5/DQwj3475ZkrD4ksrkXcgqh0+lr2iUAoFyrw8yfjmHOuhMo1+pu6mskIiL7xUzsraD742ImrzObgYsHgc4Pmx5TOQHaMuD8dnHZv/vjIpt6ZrN4vO2w2rcvt9kCTKUEgGmgV52C2EqZWI2nuK0tiNXpgKwT4n72GSC4a+37qi+9Hkj4TpRJtKvDz6UhXDklfjaF2WL/zaxHq5tGhVeHd7D6mI+hh+3s+zri9OUCKCSglZ8rfv8rEy/9eBQJqblISM21eE67ADd88FAM2ge5YcOxDFzIKcLdnYPQys/VuM6CP8/i+32pAID8knK8P6rzzetdq9eLE7rgboBbwM3ZBxER3RQMYm8V3hFAzyfFlzmVC1CSB/z8tPj+yikgZoxY5uwDtOhe+7a9zDKx8qAuoJ6Z2ErlBCW5tTwvE6gQ9ZjG2cJu1Lb3gWOrgInrTZlhADi8FFj3PKB0BF5JtSy/uFmuZ4hbvVZ0l3B0rXn9ZkiSJLQLNHUqGB4dhOgQD/yUkI6L14qQkVeC9NxiXLxWjFOXr2Pkgl3wcVEj63opAOCjzacxOCoA/xzQCmqlAv/9I9m4rR8PXUSguwbTh7Sr8/FodXpcvFaElt7OtQe/yfHA8jFAh3uB0d/Z9sKJiKhJMYi91Tl5iVm9FCpAVw4kfi++B4A2g+tWh2meifUzD2LrWBNbVgiUiWlTbS4nuHredL/yFLv1UV4M7PxITLRw9g8R0AOi5va3l8V9bSmQ9RcQEnvj+6uNHMQCIqC3wyDWmlBvZzx/ZxuLZVcLy/DGL8fx61HRx9bX1REdgtywIzkbG/+6jI1/XYazWokKnR7DOgViQFs/vLLmGD798wyuFpVh5vAOWLzzPL7dk4IAdw2GdgzEyK4hCPUWtd+HU69hya4UbE++gtyickzsE45Z93Ws+UDlWu+cczfjx/D3krROnCT3+3ezu6JARLcmBrG3umHvAqn7gC5jgU2vAX+tEcEbALQdUrdteFeXia1jdwI5yHVwAhwNGbu6tti6doNBbFkh8P1DQMQAYODLwNk/RQALALlp4ra8GPjhcaDCbFKIjCONE8Sav6aSPMCjxc3fZxPxdlHj07Hd8GBsFvKKyjG0UyA0KiXOZF3Hwm3n8HNiOorKtPBxUePtkZ3g4+qI6yUVeOe3JCzbl4qfDqcbB45lF5Thr0v5+HhLMsb1DoNCkvDN7vMWneSW7E7B7e390b+NL/44mYVTl6/DXaNCpJ8L4iJ9RJbWcJKkK8xCeYUWZRU65BSUwdlRWWV6X6qBXg+sfU78P7cdCgR2qnn9oqtiBsCWvRrn+IjolsQg9lYX0V98AcCgN4GTv4oaWYUD0OrOum3DvYXo76otEwO7ZHJWtbxQBIvqatoqFZi115IzNObdCWqqBbXIxKbX7XjNpe4BLuwSt50fApLWmh7LMwSxpzcCV5JEeUTbIaIu9lKi7fuqD/NM7N9k4ofb2/lbfN/a3w3vPxSDfw9uh3VHLqFPax/4uDoCAJ7sH4k2Aa54YUUi8orL4eWswozhHaDX6/FL4iXsPpuDJbtTjNt6oGsIHunVEuuOXMLSPRfw0o9H0DXUC7//lWmxz38OiMQrQ9vjyoUT8AegL8hG+9c2QG8Y66qQgJFdQvDCoDYI82nm7cLKCoH104FODwJtBjXNMRRfM52QXjtfexD789OiFvnx34CwPjf/+IjolsQg9u/EKxzoNRnY/YkIbDV1nFZU6QBM3iH6zZrXiapdxSCxihJRF1tbEOsWaFomB7HaUpEJNe9Ve367CJbdgywzsXn1CGILDPW6eh2wcz5waoPZ9gxBbI6hBrP1XeIr4TuRiW0MlTOxf2OBHho82b9qy7eB7fyx/vnbsCUpC3d3DoKvIcAd3aMldp3Jxnu/n0RucTlm3dfRGCB3CvbAzuRsnMsuxO9/ZcJBIWFIp0AUl2nxx8ksLNx2DocvXMOnV88BEqCU9PBEAa7BHc5qJYrKtFiTkI5fjlzC0E6BeLxPOKJbeMDRQQmdTo/c4nIcT8/DjuQruHitGG38XREV7I4Adw18XBzRwssJCkUjXVL/6yfgyDJRGtFUQey1FNN9+QpHTdL2i9vUvQxiiajeGMT+3dzxmpiuts1dtj3P1b/qMkkSdbF5qSJYNK+dNSdPdCAPBANEAKxwELOGFV8zBbEpO4Fv7wXCbgMeX2+ZiS3MAirKAAd13Y/bfDaxw99aPiZ/2Mr78I4EgmLE/awTtu+rPirXxJJVLbyc8Vif8CrL+7b2xS/P3lZluZNaiQ8fjsH4RfsR5KHBR6O7oFOIaD23dE8K3vjlLxxPyUCAJtf4nO3/ioZDQAc4qZU4ejEX8+JPY+upK1h/NAPrj4rfk9pBgQqtDpU7hf1Waf8tvZ3xj77huLtzMDycVFA73MRuhldOitusJHFCqHK6efuqjkUQm1rzuoXZpimv5c4jOWeBr+4Q3VMGzboZR9h8XTklrsKwtILIZgxi/24cHKt2MLgRrn4iiK2pQ8FVw6AZ8yBXkkSbraJsEcR6hIjlZ/8Ut6l7xNS45plYQAR9XlUb9FfL2nFFDgTObQXyLopSBmMQGyGOUeMhsqJXTgJBneu+r/rI//uVEzSWri29sH/mnXBSKS26FEyIC4eDQoEtW/8AzMqg3SquAYY+t51beGLJ4z2RlJGPb3adx9ojl1BSrkNZhalvbYinE/q29kEbfzecvnwdpy9fR3ZBGa4UlCL1ahFmrTuBWetEkBbkocHUu9piVLcWDZKh1ev1ptd05bRhoRbIPA6E9rjh7dvM/P+0tiD2yinT/ct/idukdeIk7sjKv1cQq9cDS0eKk+1nD9Q++QwRWWAQSzfG2Garhg4F8iQMPq0slzt5iSDWPAOZtk/c6rWi/ZFcZ+fiL/aRf8m2IFYuJwjuBlw6LO73eho4t00M5CrMNgXZ3hEiuA6KESUNGYk3N4gtvW7q2gD87csJbgZntYMIFP6cK363A18BAIzt1RJj3byBVWYrWznh6RDkjvdGxeDdBzqjoKwC+cXlUDso4OWshqqa2ciKyiqw+tBFfLM7BeeuiEGEGXkleOnHo1i2LxUvDGqDAW38oFBI0Or0kIA6B7ZpV4vw+i/HcTLjOhZP7IGoYHcg2ywovJTQREFsiul+Xi1BrPnxZp8WU2LLMwhevySu3DSnnr0leWLq7ZvRceFaiqlbTPJmoNdTDb8PolsYg1i6MXXpFZtjCGK9rQSxgClQ1ZYD6YdMjycuM+zDX3RFKMyyfXCXHFx3fxz4y1N8YLa+U9TnXs8QH6gFhkE/cj9cYxB7k+tizbOwAMsJbpbkTcC2d8X97v8wlcZcrdRWS/4bvpQosoXRo4wt6BQKCe4aFdw1qlp356x2wPi4cIyPC0eFVoeC0gr8cPAi5m8+jcS0XDz+zQFE+rpApVTgXHYBJEgI9NAg3NcF3cO80LWlJ1p4OSPQXQMnQ2Y463oJfjqcjk+2JKOwTHRomLIyAWv/GQvNtQvGfVekH4ak00NpCIrPZF3HphOX4eviiHBfF4T7OMPPzdFq/1y9Xg+dHsbn2sSWcgLzTKyuQnwvB7GAOHl0q2PnlJvt1G/A8keAu2YDfV9o+O1nHjPdP7uFQeytSG6ZwrZzNwWDWLoxchBbUE0Qqy03fcD5tLZ8rHKbrcxjltPjnt0ibr0jAPdgcd/mINZwXG5BwPifTMs9QkUQe367+F7jCTh7i/tBXcTtzQ5ir1dqGcZMbMPTaYHNs0zf56bWHsT+MFFcHj+8FHjgS1OpSz04KBXwdFbjyf6RGNElGAu3n8PKA2k4l11otpYeqVeLkHq1CNtPW/4feTqr4O2iRkp2obEOt0e4F85nF+H05QIs/XULnoKpQPfskZ0YczweT/VvBUkC5m06jbJKU/e6qJXoGOyBPq19oFIqsPVUFv66lI/ici1UCgXGx4Xh5aHtIUnA78cz4e6kwoC2frCmuEyLnWeycVvWORgrcUvyxJc8/XVl5kEsACRvNA3+BEQ2ua7t/xpCRSnw4z/EWIGhcy0f2/cFAD1wZstNCmKPmu6f39E4dfh/J9oKAHpAWfvJ501z4Gvg9xlich3WPTe4eo02WLBgASIiIqDRaBAbG4sdO3bUuP62bdsQGxsLjUaDyMhIfPHFFxaPDxw4EJIkVfm6++67jevMmjWryuOBgYGVd0WNTQ4IqsvE5qaK0gAHJxFImjNvswWYRizLwa7e8OHrFWEKJGztFSsH1y6VPoQ9Q8XtuW3i1rwWTQ5iM48b3gRvksqZWNbENryjK02DhwAg15S1NNVqGzLwhVeA8hJTfeeFncAXfcV0xw3A312D1++Jwp4Zd+DjMV3wzcQe2PHS7dj58u1Y9c84zBnREffGBCPS1wVOKpGBzS0qx7krIoDt2tITcx+Ixsqn4vDeqGgAwLFE8T+TqhN/361xEcVFBQjd8gzu+WMwvLTZ6BnhjX5tfBHq7QSFBBSWabE/5Srmb07G+xtP4UDKNRSVaaHXA2VaHRbtPI8HPt+Fu+Ztw3PLEzDxm/3YdcbQC/rMZmD1k0g+eRxTVyYi9u14PL10L9QF4uSyQs6LVOpQcCGnEOezC6HX60UJAQAEGkp1Di+1/EFdSmiQn3ednVwvWg/uXWAahAoA1zNNJ7mVa/MbSoZZEFteCFzc3/D7KC8Bdn1Se4b8VqLTiS4zH7YFvugnvm8qh78VEw2Zd8ahBmNzJnblypWYMmUKFixYgL59+2LhwoUYNmwYTpw4gZYtW1ZZ//z58xg+fDiefPJJfPfdd9i1axeeeeYZ+Pn54cEHHwQArFmzBmVlZcbn5OTkICYmBg899JDFtjp27IjNmzcbv1cq6zDbFN1c8jSy8gdTZTmGAMCnFaCodM5UuZwgba+4jRkD7FtoCoy9I8QUuYAYjGVN4jLRPP2O10370elM26gcxHoYgtj0g6Z9yLwjRfeEsoKbW2MoZ2JVLuIDzF7KCc5sFpmF+z41ZRaupQAqZ+tdLJpKeQnwx3/EfQcnUQNt/kEuD+gL7SWClMJs0+MqZzHIL+sEkPA/cTnZmrJCEexUrveugZtGhRFdLLO7Lbyc0TPCGxPiwgGIS/v5JRW4nF+Cy/klCPdxMc5MBgB3tA/AuN4t4XvwRwDASaeu8NYdgmt5DtbEHELUKfG/9H3bHWj1+Dhj+UBphRapOUU4eOEadp/NQXmFDre18UXvSB+4Ozng8IVcvLz6KI6n54sfm0JChU6Pf684hD+674Hz3o8AADsS8vFTxQQAQC+PAihL9SjRq5CsD0G0IgXL43ci5s6WCPbU4D/rk/DDIfF/G+JUjl16w9WUTg+ITKT8Mw+KEVc/GqtHs+zIctP9c1uBmNHi/l8/mU6k8y7enCypnIn1ihB/g2e2AOFVu23ckD3/Bf54W7yXPfRNw267OSrIAlZNEIODAaAoR9RpV9c9R1sO0WfvJlyYLr4mkiFA1Ss/1CBszsTOmzcPkyZNwhNPPIEOHTpg/vz5CA0Nxeeff251/S+++AItW7bE/Pnz0aFDBzzxxBP4xz/+gQ8++MC4jre3NwIDA41f8fHxcHZ2rhLEOjg4WKzn52f9Ehc1osiBYkrby8dFi5/KjPWwVkbdmgexer2YWQwAQnsDLeNM63mZlxNYycTqdMD6fwM755neuAARFOpF/WDVINYwM5auourxKRRAu+Hi/s55VfdXnfxLYtYi+TXX5rqhFte/veF4rZQTFGTVnEXQ68XsR40p4Xtx0rLt/8T31y4AC/oAiwbDYsqspnbuTyD/orgC0GOSWGY+S1u+4YRIDsQLr5hKX7xbAX2eE/fN/6YqW/cC8N9YIGVXgx66JEnwcFKhbYAb+rXxswhgZXPu64SJbcTJ/+AB/eEaIU62ok6b3otbX1wDyezEz9FBiTYBbnikZ0v895Gu+GJ8LMb1DkNrf1f4uzpiaHAx1v+rFx7oGoIXh7TD3lfvRGt/V/y75FNjAAsAPRQncV9MMNY80wcrHjJcEfOOQJmrODk8efIEhn+yA7Fvb8YPhy5CkkR7Mt8SkQkvUPmINnrmYh+HXlIABZn4eftBfLDxFBbvPI/SCq3Vn5FOp8c7G5Lw3PIE5JeU2/gTNrieKQJHmTybIQAcNRv1p9eZ+ko3lIIrhhZ7EhD3r6r7byinN4pbeWArAJzeBBz9oeH31dQKrogWjal7RHJA4ymWX6kmyVKSB3zUEfj6DqC0QCxL2QVseMkyK1+dM5uBj2OA91sD70WK4Nm8DO3CHkAu97l6k7L5DaW8GPjlWeDU7019JDaxKYgtKyvDoUOHMHjwYIvlgwcPxu7du60+Z8+ePVXWHzJkCA4ePIjycutvPIsWLcKYMWPg4mLZPD85ORnBwcGIiIjAmDFjcO5czWc2paWlyM/Pt/iiBubsDbQx/H7N3/Rl1XUmAExvMMXXRKbj+iVAUorpXsP6mtbzriWIzUsz1dJeMAsm5OluNZ5VMyiela4aeEVYfj/gJUBSiEtAdb28uf8rcWl085t1W19+LX4dxG3lcoLD/wM+aAPs/LD6bSR+D7wXAez7svp1zv4p3tgb6LK48Xd67k9REnHoG5FJvnbelHlvDuROF60HmaZLlrN+8mAoR3fAz3ASYR7EeoWZTqTSD4s3+MoqSsWlaOiBE7/cjFdQI4VCgmeh4YPRt62pDEYu3wnuKi5j1vVELHEZ8EkXtPhfX8wL24t/3RYCX1dHfHG3Dx5UipKx/1Q8CgDopEzFJ/e3QreWXpAMPzONXyRiY0Sf5T4+hXBSKaHV6dHKzwU/PNULx98cjFd7iqtnR0oCsT3P12L3q69FIkUS/5e//r4Bn/55BnN+PYEnvj2IorKqZT3vbzqF7Tu2IuvoFkz+36Eqwa6+LidUR1eJn5dcv3v2D3EilnNWBH2SEnAzvPfcSBBSmC1+vqVm3UjkLKx3JNDhXnE/40jt03jbtN8c4KLhatO1FBG0lRUBK8cBa56o+wl3fWnLxYm9tc+GhlaYI97nrpwUv7PJO0SSBbDsiGEudZ+ox844Avw6BUg7AHz3ILB/IbBirPgfr05ZIbD2BfFzLbwiMr4nfgEW9hevWa8Xfc9lV881r5P8yk7/Lq46rZ5kSrDIrmdWLX9rJmwKYrOzs6HVahEQYNn+JCAgAJmZmVafk5mZaXX9iooKZGdX/Wfdv38/jh8/jieeeMJiea9evbB06VJs3LgRX331FTIzM9GnTx/k5ORUe7xz586Fh4eH8Ss0NLSuL5Vs0flhcXvsh6pZQ2M5QaVBXYApE1uSawo4gjqLiQ/CKmdiDZnTgsuGyz9mspNN91PM6rPlzgSVs7CAqZxAVjlT7NsGiDa8rj8rDfaojlxvefbPmt/8ZPJEB/6GINY8E1twBdg0U9w/sRbVOmVos//nf6ofGLbvC1Hbd6wBPkjkD3hAZKcSvrOsaUy7CTV91ui0wM//AvZ8Vv06aYYR76E9TSctcjbNvK2ai1zXnW36HXqFiy+3YBEIyoGAxfb3m06ezv15I6+mfnRa0/+Xb1sRtMp6PgEMNpRSHP5f3S5lnlwvbvPSgN9fBr4bBeh0aH1hJRSSHuc8euHRKe8DXhGQ9DrT79oY+EcYf85DQkqR8MZd2DilP357vBW6/xgH9aox6OUkSgnO6IPxzA+nkakUdfJX9a7495brOFgmnn+PbybG9AiFs1qJHcnZGPvVPqzYn4qdydk4nHoN3+w6j5VbE/CDejZWOr6F8nO78M//HcKcdSfwjyUHcMcHW9Fm5m/o+Z/N+MeSA1i47SyuXK/0P6nXQ28oJVjt/hjKFBrxnnH5L+DICrFO5EDTz7Wautj03GJ8siUZyZevW30cAK6tmQ78/DS0C283DWyTg9igzqJbSkAnAHrg/Laaf0+2OPsHYDbwD5nHRAcYreFnYR5k1ebU78AXt5nGEdTFuW3i/WH99Js7vgAAtswW04e7BQETfxWJE7924rHKgwll5tnpYz+IILjCcMKafhD47aXq97dzvria49ESmLwTmBQvPjMkhXjNKTstP4/KCy0HMDY38gl+WQGwuVL51N7PgXntgS1vNf5x1aJeA7sqt2exaLxdx/WtLQdEFrZTp07o2bOnxfJhw4bhwQcfRHR0NAYNGoT168Ub7rfffltlG7IZM2YgLy/P+JWW1sCXg0hoO1RktPLSql56zZGDBSuZWPNyAvnNNLS3uA3oBESNADqPAVx8RU2sUg1AbznLFWBZj5u23xRAyvWw1uo0PWsJYgFDNlYpRk9fPFT18crky7ZlBXX7cMivFMSWF5oC9M2zTEHp5eOWGRxz8gdhSS6w9wvr68gDm8yD/foqyBKvT7b9PZGBkN2MgSnWZB4FEr8TP6eKsqqPm7drC+1lCmJzUw0TXMh/l5Hi7wsASvNNs195hYuWOPLJ1AXDlaaKUhE8AqJ+UpZ9uvZpkfMzRNBfVljzeuYqSoFfp4ksf+UTxGspIhhx0IjX16K7uO/oDvSdAoT3FdNL68qBJfeYJhawRq831Yf3elrUhF/YCez9TGRnAEQOm4JwXxfTVRL5qocxiA03nRzmpkGjUqJdoBvUiUtFcJi8yTDaHyjxaI2C0gocKRO1wQm6NogJ9UKLTmIK2pH+WXj3gWisfCQMEzXbcWfGl3h7zT6MW7QPDyzYjdnrTuCfDuvgJomAY5Z6KbafuozFu87jj5NZOJddiAqdHlnXS7Hr5EXM++0o4uZuwT//dxDrjlxCak4Rfvj1V0hZJ1CqV2F2ajR2lYuA58KGD6Hf/Yl4HV3GGuvly7PPQWs+VVv6YWRveBsPfbYd8+JPY9jHOzD3tySLrHFxmRYfrDsMxzNiUI/yajL0X90hThjk9lryADc581/De01BaQWSMmq5oliQZSrtSt5k+VjmMdPYA8DyylVt9n4mnr9yHJB1sm7PkQP/0ryb+96Qn2GqbR612HTlT74CU917n/weESAGSqKiWJy0PLwUgAQcWmI6oTF3LQXY9bG4P+Q/QGC0OFl+8Csg9nGxfMsc0+9YzvRfPSf+/78ZDmycWc8Xe5OYD8Y8ssz0d6jXi4GPABAQ1fjHVQubglhfX18olcoqWdesrKwq2VZZYGCg1fUdHBzg4+NjsbyoqAgrVqyokoW1xsXFBdHR0UhOrv6D2dHREe7u7hZfdBOoNCLgBICjZv/w5SWmzJfVTKynuC2+ZqpLa3WHuFUoxRvJAwtFMKFQmLobVC4pMA9iK0rE5V/ArDOB5WVLAICjm6mcQeViPdD1aQV0vF/cP7mu6uOVmb8JyHVo1dFpTWflcrYAECUFqftEgAaIgESvs+yjab6u+UClPZ+ZBsnJSgtM6+Q0QBArZ/5c/AGlI6A1BJBytirNynFW59RvwJp/mmrRbCEHjNoy4PKxqo9fPi4+kDQegE8bUw10eZEIuuXLi96txDoKQwseOeMqDwIJE0EVUneLD8IP2wHfPSDe2OUgVjK8jdaUjb2UCHw5APjlX8Cqx6qvcy69DqSaBRkn1gIHFwEbpgPLHra81Cz/3fu0Ef8vLr7ApE3Ak3+Y/uZHLBAf5PnpwKIhpmC8srw08feocAAGvWmaNWvTa4YZ9Vqa2l5VDuzlQMUr3PJkARB/54nfm/ZjGCj1j/uH4btJvRDUU/x/dRs2Eb/8qy/ibjNMh30mHpjjjeiVvTELX+A5h58x138L2vi7ItTbCdHuRfiHKl6sq1Sjo5SCOaGH8e8eGizvfhrrh5dh9+TW2N17L465/AvbnV+Gs64AG/+6jOeWJ6D/+3/Caf+nAIDNUi/c3bMD/nKKFS8vdQ2kihIcVsdiVUkPFLuK17Rt7370ffcPLNh6BgdTriJ3+RPw3f8+ehf+AQ8nFSp0eizcdg6PfLUPRWUVuF5SjtFf7kHKnjVwlkqRpvfDHm0UpLICYMWjpvcIOYgNEfu3yA6ayS8px32f7sSwj3fg1Z+OVVsrjP89ACzojQ3fzUPeMcOVmogB4jbjqOXVkur+HiorLzGNWSjNr/q3WB3z/sGVA+qGtHeBeC9oGWf6nwXMgthTVS/l6/Wmz4p7PhInb63uBMauEp9nA2eIx/58p2oWOf5NcQIZMcBUCiLr+7xIflzcD0Av/j+Du4nHrp4TV+ou7BInpjcrO11RZvmzrws5CSN/Lv7+svgZXTkl3veVaqC1jdPVNwKbgli1Wo3Y2FjEx8dbLI+Pj0efPn2sPicuLq7K+ps2bUL37t2hUln2blu1ahVKS0sxbty4Wo+ltLQUSUlJCAoKqnVdagSd5RG9v4g3PMDw4aYXgZi1QFLOxOamidGjSkeRPaqOHIhU7hUrn2U7GDpVyllQY2eCakbMy9lYeaYuayINb/7WLieb05ZbZohP/1Zz/VNBlqjFk5QiOHc0nGCV5ImMBwB0GQe0Gybumwc2ssuGUa/uLQD/KJHt2LPAch3zy2g5Z2+81YwcxAZGA+0NLfAUDsC9hsxV1gmgpI6155teFyc9R61kOmpjfiKTbuVDXw6mW/QQJ0AOjoCrYQBSbqrp8ZBY8buXS07kDhFyENvS8L6Wtl/UuRVfE8Frwv9MwUbMI+L2bDVB7Nk/ROZFPmk5Ew9snSs+NHZ9LGZqkv00GVg8REy/CgBJZqUkZ+KBhQNMWWS5VtuvrWmdoBhRCiPzDBWBbdhtYna4X56tWo4DmE6SAjoBKieg+ySghdnVsB7/ME78YAwS0g+JWmG5vtgr3PQ/VXxVnJyc/UP8vzp5mf5GJAVUgVG4rY0vOt/zL2D6GXjFPSYeC4w21YjrdQAk41WSex32I35qf+x46Q6s67IfKn2ZyLIPEpc+x139DM8dewBxx2eh4x8TEbykJ4ITP4FaW4gA3WVs7bEXTw9shXAfZ7SS0jFcKYKyO594F3MfiMaTE03Jk2y9O/6ZPwkvrT6OyevFlYYWuIzM/BK89/spTFm4Fp4FoqzmHrcz2PbiQCx6rDu8nFU4kpaL2d+ux6tLfsPRi3l4QC32U9r+AYwrn4FlFXcA0JuuaAR1RrlWhyM68Tr1lxKrBDc6nR5TVyQaZ4Bbti8VoxfuxY7kKyg37wOcf8l4Ujf8zGx44Dry9C5IDBSdgJBxBFo5GAXEyYvZZBnVunhABG0ufqJsJPeCqBmV3+urYxZIFZ/4HbvPZNetVtkWxbnAQUPXhb5TLB/zbQNAEv+3lYPu3FQxY6TCQfzdDXsXGL/GlNDo85y4Aph7wfL/MCsJOPGzuD90btXPDq9woNODpu/DbzNlhq+eM2XCtaWWLf8a0pbZYsDZ8dV1f44cxA5+W3RnuXhAlLYkGRI4kQMBTfNLBNpcTjBt2jR8/fXXWLx4MZKSkjB16lSkpqZi8uTJAMQl/AkTJhjXnzx5Mi5cuIBp06YhKSkJixcvxqJFizB9+vQq2160aBFGjhxZJUMLANOnT8e2bdtw/vx57Nu3D6NGjUJ+fj4ee+wxW18C3QxhfQH3EBFIJRsyDOadCawFiXIQK9dshcUBapeq68nkwV1/zhXtb+SATM6qRRveOOQ6JLkmtrq2Tx6GrFF1rVcAIKS7uE0/bLqMbE1+OkRTbbUIxnNTTZemrZHba7kGiOBAPvstyTWNpO10P9DSUF4hl2mk7jVlAOXWLUExQL9/i/tJlTLG5j1Sy4uqTrBgK/OBej2fEkF413Girs+zJQC95axr1SnMNmWGL9Qw+r86cmcBwPoJhnzp0jwQk7OEmcdE7Rwgglyg0kmWZLos7tde/J2WF1mWyqyfLoIs37ZAFzHYCee2Vj1JKL0OrH5ClIpEDADuNgzS2/4eMD8aiH8DWPGIyCxnnzFdttu/UAzAOWMIcEd+Ia5m5F8EltwLbP8A2P6+eEwuwamOkxcwdgXg7Ct+f+aZUePP65Dlz0OhAO77r/h7VrsCXU3v6fCKECdeunLRf7TUcNLi2VJkteVLp3lppnrpzmOA2MeAR1YADy0BXA0nDZIk7svvDw6OwNO7gZcvAP8+DbxyAfjndvE/dfWs+HvOTTUFLXe8DvR8EvBtZ6jzlMTvXC4P8msPDHgZAOB9fAle7gb8OX0gNnbbBwX0QPt7oGkhLiU7BkUZnyeN+BRPDOuNIA8NUvTiKmNrVTY+HNUZMS08MMLluPHHMVBzCp7OatzZIQBfP9YDLRzy8MbFJ/FuxhMYoj6GgYpE8fw7HsPE21rj1YpJ+KBcdN45rwtE70+OoduceIxclYXreidIFcVY8MOv+HZ3Cr7fdwGLdp7HtFWJ2HIyC44OCrx2dwd4OKmQmJaL8Yv2o/vbm/HWryeQlV+Cv/ZVzXZu10XjuW2Gn2/WX1CW5qFI72gMmn9csxInM2s+8dSdE/1yD0jRSB22RPyO0/YB6563OFnfey4Hb/xyHKk5hlpxswDZ6WoSpn29AcM+3oG1Ry41XDB74GtxguYfZRpkLFM5mf7vKw/ukk9CAzqJq4mVqZ2BHk+K+7s/Mb3OHYb/4Q73AQEdrR/TbVNN98NvM/09Xj1nymgD1dfq3ii5rnrbe3VPXMjTRbfoAXQdL+7v+th0FbJyxrmZsLkx2ujRo5GTk4M5c+YgIyMDnTp1woYNGxAWFgYAyMjIQGqq6RJnREQENmzYgKlTp+Kzzz5DcHAwPvnkE2OPWNnp06exc+dObNpk/ZLDxYsX8cgjjyA7Oxt+fn7o3bs39u7da9wvNTGFAoh+CNg1X4xEjRpR86AuwBS4yVoPqnkfXcaKS1I5yWJWpb5TxCw6csY19nFRc5i2X1xOqamcADCdHcuj063xaweo3cSbZFYSENjJ+nryWaxHqHjDOhMvRnvK9a6VyfWw7oYrCRoPIA8iYyBfnvVuJU4MABFkZCWJ2ka9FnjusFlNXbTISAHiZ2Pez7Jy27PsZFNGuz7kExOf1uKk48Uzpixyix4iwLh4AAjvJwKu6k4Q0szeyC/sFh8QtkzLaJGJtTboSm7XVimIvbgf+GuN+N470hRMmZ/ouAebPtQUCnGJUm5UPuAVYP+XItMIiOxEix6iJKUoW2THgzqbtrVvoShf8G4FPPqj+L1kJ4vaUL0OcPQQJ347PhAnBMbXdEj8L5UXieOOGSNKbZbcLX7HfxgGWHQdL6bSrY2jmzjR2TgD2Pp/4hL276+ImtvH1poysS26m57j3x54apsol3AxSyxIkviZ/LUG2PqOWBbWV3zoyz/nzGOivEX+uXUzfCjKVxZqolCYSo1krQcBp9aL0d/XM0QAHTEAiOgnHh/3oyhJaj3IlA2uKBVBuCSJeuCTvwK/ToHU+WE4nDD8DQwwG7gjScCEtUDhFfiEdMM/AfzjtggcTb0C/dIX4aAtxoPtVHiw+23A9x8DhnMwRV6ayDh6hSM2zAtLOh2Fy0lRl/+F9C4knV68x/hHYeZwIDrEA1tOhuDR07E4X+aMzDKRzfR20eAMWqOr9hguHNuJlYlVZ5ma+0A0HujWAkM6BmLB1rOIP5GJ7IIyLNp5Hkv3pOBVaQM6OgA/KwfjzqByuF38E5mh9yDtnDdy9S7wlEQm94KmPc4q2yKm6By053dh6PyOiGnhgZY+LnBSKaDTA6UVOqiUYrrlB4+sRzREB4nD6/Pw6wNLoF7xkJhMxLct0H86tp++gie+PYgyrQ5rDqdj9r1RuP/qeSggMtu+Uj7ucDiCZZk+eH55As5mFWDqXW2rvMba6HR6KOSpkXPTgJ2G1m99p1TtRQ6I48u9IMpvzHvwyldwQrpVv7OeT4r/w0sJ4gqfe7Apu9m/aiLOKCBKfDal7ReBtVx7fPmE5QDBKyeB9sNrerm20+lMnWiunBSfRbXNgFeSbxqD4REiWr4d+NrU8k0yazvZzNSru+8zzzyDZ555xupjS5YsqbJswIABOHzYep2PrG3btjWema1YUY9LjtS4Oo8W//CnN4repXJwUV0jeKWDKUAEag9iW90BvHBUtAza9bHIxrQx1Oi4h4hLw84+ImhIP1R7OUGf50SAK2fSrFEoxZvc+W3ig762INYzFGg3VLxxJG+2PCM3J9czehpOwuQP7awkUdercBABsaQQAW5Jnqij0xkuBR/70TSoKzBaBKaO7iIrlpNsyhAYM7ESAL04sWh1e/WvtzbGINbwO5Wn6gVEBuz4aiA5XgxcyUgE7l8oArDKzMsjrl8SQYB3hPig8G1nCi6rYx7E5pwRwb+ju+l3npsKQDLVGQKm4EaehUkO/AHLDhaelU6MIweKYCyoC9D/RXFJbeOrpscc1OLDMXmjaK82+D/iQ6wkD9j9X7HewFdMJxZD3hFBn187kZFeMlx0EJCnxvRuJbKOcqa1w30iwHILAB5bJwLZq2eBftOBO16re/Df/R8isMy/CHxl9jew7T1Tb0s5EyurbiBH+G2mk4FujwGDzUYt+7YTQaxhQBhCYqvPWNVV1AgRxB7+n6ks447XTI97tgS6P275HAdH0/3Bb4u/y9Q9pox626HiKoY5z1CLQZ8qpQKxEQHi/yv3gmiz5ehu+htyDRDHk7JTnLCVF6N1qugCUu4aDFWB4e+004OAJEEhASO7hmBk1xDo9V1wtbAMadeK4aCQEBXkDmnLTmDXMdzvn4kC3yCUa3VwVCnhpnFA31a+uFudAGz4BKGD38LcB6Lx9shO2J58BZ/+cQaHLlxDD7XI7A2952FoYh4EclMwyTMCnVOuQr+xM3BZvPYOPe9C+9CewLIfcLvTaTgUSjhyMQ9HLlbtcKJBKV51PAlIwAl1Z5y+XIB3TobjtaHvw2HDVFRsfR/LcA/eiT+PMq0OXs4qXCsqx9s/7sSDGlEysVx7B55z+Bmz2l+Cp28rLNh6Fh9vSYZGpYSjgwIbjmXA1UGLN3NfA5y9cXbgAnQI8USIp5PpQPR6nM4qwOPfHECknwsWjO0Kt/XTRFlGaG+RRLHGr514P67cK1YOYoNrCGLlz4eDi4AfHwecvMXJZ5shVf92Krtrjum+nImtnA2uTyY2bb/4Px70pmm7Op14H5AkkVGVOywA4rPSWhBbViSuVnqFm0r0NJ7ihNfRTYwHOS4mU0HLuOqTQU3sJkxRQX9bAVFilOflY8Cap8QbB6Sag1MnLxHEuofUnBE1ru8J3PmmqBksyBSZLkDUPkmSyACe+Flchq2pxRYgsm91mQ+9RQ9DEHuw6gelTB7U5dHCFBxl1TAaXK5nlAdEyZdg5UtcnmGmGWRCe4sASb6UD4jRo3LgHBgtXrt/B5GBvHzCLIg1ZGLD+ohsQF07FOj1YlRuUIwpkNHpzEb1WzkxkWc2Mx+FvGUOEDWy6uU6YxBrCK4v7BaBz6rx4kPlyT9qDs4qz9yWflgEVQnfiRH6gLi8aF7DJV9WlGdhMs/Smr9BV84exz4uttluuPiddJ8k9lN8TYz+B8TfRfJGkbn4PE4Et47uojzEt51ljZxCCUTdJ+77tQMibxeDwnTl4piHvgssvc90nB3uMz3XPUi088lLsxwQWBcqjQim1z4rvg/sLE6E9ogBTnDyst6lw5qYR8QJQ/htVWeYGvyWyH7Js6DJpS43ou0QMfhOLodpM8Ty91cb7whRHnFkufi7UruaBq/V9fm5F8RrKs0XJ5oeoSJw2jlPBLFdx4k2TUU5gEdLqCZvB5aNFv+PVk7kJEmCj6sjfFzNgm1DVrC3Ywp6P1opuMo4Anz9mBjAFNwF6DIWSoWE29v5Y2BbPxw5dxEdv0sF9IAmso/ISnpHQgGgV6QPEBlrDGIR2gtSy16ApIB/+SUcHpmL3UUhuKQMRXGFDgpJgtpBgXKtDl6Zu6BO0kLnFoypdw/F40sOYsnuFKzWBCNe74VA3TX8/vsvKNF1wh3t/bHg0W74cvs57Nwmysqy4A23rvcDx36G+sI2vDQmEhqVEhs3b0TUlncxt2IsTupbYpDiECLUiUAhMPf7hdik64GeEd4Y3T0U9zjsg3rdv1CkjUCn0sFIygvDys9W44mCTdAr1TgX9w4Cy3VwcawmEwtYBpA6rTjJNvuZV2h10EOcuFjo+4I4ib2eYTpJrikLa41nGPSQIBlK5yocnOFQUVR9/9rqXD0HfP+QeF9x8gLunS9Olr+4TXxmTPzVFKy7BYn/vwu7xAQ1ob3E/7ecrf55sig/e2KzacIc89aTfZ83BbHt77HtOBsRg1hqWJ0fBuKPGQJYiMxVTR82Tp7izLH1nXXPKCmUQMeR4pKsXHAvv1G1Gy6C2JO/msoJasvq1Ua+xGrtsrVM7sLgEWoK8IqviTcH82ylTH4DDe4ibuXSCjk7YB5MtOxtqjOOnSgCeDmYdPQwBWf+USKIlbOvRVdFoA+IeqYLu+reoSB5k3iTc/ETpQsad5HB05aKYKLyZBGACIocNOID3itC3OanizY1vSeb1isvMb3+qPvEJeILu0wtoC4dFnXNcoBYmU5nGkQX2ku85r0LTPWjFYbBJnKjc5lHpWOuLhNbOYh1UIt6TplKAzz5p+k+IC6TT94psqcn1lq23xr4imlQlDV3vGbqbND7afG65Wysa2DV7Kja2fYAVtZlrAiyXANEYLXkbtOlzpDudf8fVDuL12WNezAw8OX6HV91nDzFFQR5hPvtr9q+jZjRpillbeUVAWCryMTKNdhtBotyhp3zgPM7xImfPLCy11MiyHj8dxF0Wqu5tEa+cnD5hBg0pzJkIksLgB//YeoGcvp38bs0kCQJXZAsTnw8W4pLwpWZZw5De4gT56AY4FIC3Dc8jaEA4N9RXEKPGWPa9xZRQ62I6I/b2wfgsbgwfLvnAq6XaHHQKQb36LfiUd8ziG5/H6be1RYalRLP39kGz/kfBVYD/i3bYeL99wHJhitKV07iuTuicftfMxF99SicHVU4ccdiDPxrGWBICL7ktBabi7pj//mrUFzYiXtV70KSKtAFJ7BQbXh/M4yLm1dyH/67NBNAJkI8ndC1pSd6hHsju6AUhy5cQx+VAs8CwJXTyC0sxdEjB9FLdQaOZQXQq1yw9pIb1m88iN1nc1Ch02FklxCM7hGKMB8XqB0USMh2xqEOyyClH4TftURkK/1wcZ8jIs+dhZvGAR5OKrQPdEdLb2f8lHARX24/h4LSCkSHeKB3pA/Gx4WhXOeAQskXAXrxmbSqpDfGOvwB3ZVTUOh0VcsgdDrg0GLxN+AeIj5XXHyA5WNNg0/liTlObxJXnnJTDbX1hsA4tJcYoHVkGfCL4cp522GiPr4wWwSwep24YiifxJu3ngyKAWLGihM085PwZoZBLDWs6IfEYBXoRVa0ug86mX8HkQ2SW3TVVcf7jT0nAZiC2LaDxaV480FV1ZUT1JU8uOvKSTEStnK9HmAZxKqdRceA/Isi89myl+W6hTmW88UDpm3Ko1XNg9jIAcAWiEDrrjlisJBclyVnYQERxAKmIFb+GXi0NM3mVNdZu+QJFgqviEFIg9821Th7R1oPypQq4K63RLA/5B1xgvHrVDEQott406C9Swniw9jFT3RgOPELcHyN5SWw3Z9aBrFXTosgtdt4EQRrywBIIkOQts8UwHabIDKluRdM7dpk5oG3+UxdQM1BrDXWgpLAaNEW7up5kRVO+lVk8KJG1rytFt1FacDVs4Zm6ZIIZjdMF0GXtTq/+lIogdummL4f/Bbw1R2m42jO5Jr4TqNMJ3+NxdArFqd/M7V3azNYBAoKlfhfX/GoGDCodhV/h4D43SnqGMACImBx8RdXkTKOmt47fn9Z/P/J5Vdn/rCsfQdMVzfMp+w2F9ZXHFuL7qZBtcM/BPZ9LradlSSuHv06RWTnH1piuCJj6JRhqD+eeXcUOoZ4oKW3M3peLwB+2oq7nU/i7uGW9f9Sboq44xUufg6BncXJaeZRSEGdEa0Qj3evOIwe7bTANkOJhqRAa+1ZHHyoAn+mlGLw0XlQowIbtd1x0aElJmq2QSovwqUKN2yv6IgvdffBw0mFvOJypOcWIz23GL8eNXWKOQEVntUAyL+IKx/0RH99ivGxMw6t8MJKyxZ9Kw6kYcWBNFQVYvgCcOVilUcdFBIqzPoIX87PwuakLKw6mAZ/Nw0mV/gjQCmC2D8c78Soim1QlxchO+MsfEPaWG7sxE9iKnVrXANEgiL3gkhmnDabJjZtH/RXTkECkOMUjvIuE+BbVgyHnNPic+H0b9Bl/gV96l4o5Ss9Fw+I9y7AOF6iQquDUiFBul9MYX08PQ+f/nwIc0Z0hL+7DX/PjYBBLDUs9yBRFH7xAPDgopozUAAw7P+AXv+0rF2sixY9xRu+XMsjtxVy8hLBs5zZcnCqueNBXbj6iTfiaykiS1g5OALMBnYZBk35GkaS5xiC2NMbxRv4Ha8DGYZSArlHKWC6lZkHsSGxYlS3T2uxXufRlkGsLKBSECvf+ncw/Xzy0iwzPNbotJZvjHu/ALpNrFoPa02vpwA8Je53HS/qsa6liEECcumG3GKmZW/Dh7RkCmDbDBa1i8kbRb2YZ5gIgnd+JC63a8tMGVZXf8sPbI0HcOcskbGwFuSYZxladLf827Q1iK2Jd4S4hG7LZfQ7X7f8vscTIgN7o7WktQmJFfs6vLRZXzIEIE5cvVvVreyoocnTUsuDKb1biZMstbP4GabtFTW7kIA736j6/1xXkqGO+/Rvoq6/ZS9xm/Cd2PYjy4AfJ4kgN3W35dUGudZX7mhSmUeIGFNgfgLWIhZo8bW4X3xNTI27+78iqP16kMjUacvESblhYI/aQYGHuxv+lwoMtdWZR8WVr+RNwMHFwKhFlpNgAKYgNuMo0LHQeFVIgl6Un5XmiysPMaOBXR/D+7en8aBhRrx8v1gktnwf98a2hjLIDZAkOF4vRef8Ehzxd4VGpURuURmSMq5j//mrSEi7Bh8XR7T2d8V//0g2Dixro09BiV6Fc/pgpOr9sTD3HqiUEp7qH4khHQNRUq7Dt3tSsOP0FeSXiDZnwR4axLXyRcdgd4R6O6Ncq8PJzOtIu1qEgtIKZBeU4sSlfJRW6ODrqsbkAa0QE+qJxNRcLNx+FqcvF+D05QLcrQ4C8BfgoMHsp8fj4qdfIFKfhtmL1iA74DZ08FFg0qAuCPF0Qu6uxfAEcEwXjhKoESLlIFC6BknlBGn096iIfxMOqbuw7LuvMOr6RsinMn9u+RXuOccQqwBm7anAul0nATyEbi09MT/gQ7S8vAU/LpyDCKTDeH0n/ZAxibLslA7vH9yEa0XliApyx6rJcVArFXjxx6NIysiHq8YBHzxUSy1wI2MQSw1vyH/qvq6Tl+0BLCDO7KNGmnqqyplYQPQvlYNY8/Y9N6JFD/GmfPFg1SBWr68axPq0FpeU5ezl+n+LANKntalfofk0oZU7NVQOFM1Hdbe6wzSAzXygmZyJzU0V2Vq5Hta/g1hfHiCWc7b6AWqAGDhQlC3WD+kOnN0ieqTKZRk1BbHmlCqRYVz7rAhi454Tvzc5YxTaW+wjMFp8CEoKYNh7gPI1UQ6y4lHRT7fUbLDJxQOmgNw9RDxXoRIB7u0zLUfRV6ZyEsFq4RXLUgKgUhDbDDqeSFLjZRuHfyAy5+YDoZor884PjSm4q/g7UzmLk/Tek03dGNoOFkGsb1tgxGe21epaE9pTBLGHvhGD8ba+K5bHjBGBc9vBIqg9vVF0aEjZIcoZ5EkMqsvEAjX/fzh5idcW8wjw0z9NpRvthgP3fWq9LMrV3zQO4sDX4mRTWyoGHsntteSBkvLvLvOoKB2SM4GAqVQr6j6gz/NiIoDyIvEzjxkD98Fv4WVjS0bBz80Rfm6mv1lPZzXiWvkgrpXla+wR7oVV3wzGUN1OJHoNRexDL+K9+EvYeuoKgj00WPVoN3Rtadp2zwjxOiu0OhSVa+Hm6FBldtHh0Zb96cu1OqRdLUKwpxM0KqVhv954MLYF3vjlOP48mYXOnbsBxzcDIbEI8fFAYasuwJk0+JdeQL+0XRh5aSdeOP4S2nbsjhcu7QIk4JnyF5CrDsH10goooYWyRAdpYTaeQChedADuvLocaum68Ti8cxIQLokSssuOYVCVSSjX6nE4NRczFD3xvXoLhuu2wgmlgARooYSy+Cr0KTshAdh9xQnXDIOHT2Tk4+Ufj6JNgCuSMvLh5azCK8Oa4ASyFgxiyX51elAEsU7eptm8ABHEbjAU3t9oKYGsRQ8xaMO8NZSs6Kp4wwXMglhDoJWdLEbSy+UGh/8n5kkHKgWxNWRiK1OqRL/RE2stL1U7e4tMRkGmCGAvy5nYKBEU+bQRHxY5yTUHsafWi9s2Q0SPzc/jRNbHeGx1DGIB8TvaOFME1ue3iuDRmDEyfNhG9BcfbB3vF1nMuH+JIFau33UPMbVvu5Rgyj7JrbCGvy9OFrpPqv14gruJLG+rOy2Xe4SKFldOnuJy3d+JJNlHANuUPEOBqcfF5XhHV8vH+jwv/paDu9W99rUm3R8XpVLZp0UdbPIm8bfZ/0XxeNuhIog99ZsIBM3LqtyCxUDCG+HsDTyyUkxColQbOytUq9XtIojd9q5p2bEfRMAPWGZiAZHNlge2Rg4UmVm5ZV3UCFGfOeZ7kSGMGWu9vtcG3cO94fPcJziVmY+RUYFQKCQsfiwEh1KvoV2gG9w1VVuZAYCDUgH3yoO8qqFSKhDp51plubeLGp+O7SbagpX0BKQUceUDgEtIR+DMOkzzPwSXa6L06039F1h/pDcUDnqccuqK9a+Mh5ujA7YnZ2PuhiSczLwOVOiwVYrGiw4rECDlAgD2aKMQpzyBaMV5KKCHHhJWvToeegcNLueX4ufEdGw85o7M3BAEVoirl0d0raCDhK6KM5AMYwxy1YFY/Xgcisq0+MeSA1h/LAMwXHyYPaITfF2b3/sEg1iyXy1iRcmCW5Dlm6x7sMggph+svjOBreSAK3WfuNxufilaDlBdA0zBgNwbN+eMZTP+9IPigxCwzLSZ19lKSstRotZ0vN80Ja65gCgRxCYuMwWLcrDsawhi934umtS36CHKOcx/dno9cNLQ27P9cFEWMXG9yLDIJQa2ZJrUzmKw34GvgEPfiixsSZ6oT5UzM/1fFIGqPFClZZzIDF7PFFmg0J6iBGLXx6J8RP4AlHvoVtcxwpqRC0QtWeXsv4sPMOFncTLREJl7uvXIJ5+VKVWWU53eKCcvkR1fNd50QhkzxnQFJHKgCC6vnTcFsNEPiRPEdsMapoZaobAYOFajVreLyQAA0S9Z5SSu5MhTYMtBrG9bMfCzrAD462exLLSXmGxgz6ci4SC/z7a6w3rZVj1F+LogwtdUVqZQSOgRbiWzfJMoFJI4OXjwK9NCw0x7cgALAIHSNUxyEFMFtx36NCRDgD2grR/6t/HF1cIyFJdrodcNgH7RR5CKxFW9dY7D0UZ3Gb46Mbuc5BUOqJwgAQj00GDygFaYPKAVsPtpMZU0AP/ej2DX4UR0rTCNk/jnff0RGyZ+Lq/dHYU314rBtoOjAnBv5+Y5OyqDWLJv0aOqWf6QCNiqm2zAVgEdTY3pM49ZBqDGQV1mkwj4GoLYq+eqZm/LCgBIpswEYJmJ9Qy1HLBhC/8oMWr1kGFGo+iHTNOSyoG1fDyXDovMZ++nTc/PPi0GGCnVptZooT2BR5aLAS2l10UTfFt0myCC2JPrTYHwXW+Z+qI6eQJxZn2nJUlkY805uooR+VdOigwUYJrBzRYuvtX3O6yuGwJRY4u6T3QUSVpnyMKatXRydBNtzc7+IUpwRiwAujzSdMfaMk6MPagoFr1L89PFCScgglb5yobSQbw/XTpsurITFCNOsi8likC9tjEUtxLz2m5HD2DEp+LExfC9JLfhM5Bbshm1ul1kvBUqvPXv56H49ayYyRKovntJl0fFjJfaUgT1GYPhAS2AdWKWQK3kgH5dTFfoJsSFISWnEIlpuXj7/k5VSiqaCwaxdGvq9U8RwNan3tYahdLU6urCrkpBrNlsXTKPUDFVprZUjL4HxMAZeVpRn9aWPUzNa2Lr2qvTGrkuFhBZEfOG250fFgGge7DIUB9cJM7KQ7qberzKA8Yi+osPS3P1vawX1NnwQZUgalfD+trejQIQl2uvnDRderyRmceImrvhH4qZlFrdUfU9oc/zoia+/0tAhyYekKdyAh5YKGrtezwhSqjkINazpWVmOKizqRc2IIJY92Dg8fWNe8zNgU9rcYKi14ouPlH3iam8938psuA1Db4FxFWqYz8Are6A0slDZLXlINZ8jIg5Z2/g8Q1iNjvPUDhHmMYGKD1CLH5XkiThzXtv8sDSBsAglm5NkiRaUzWksD6GIHa3ZaYw10omVqEUHzxXkkyZ2ttfFQOTCi5b1sMCluUEttScVmY+w1L/6ZbZSs+WwJNbxH29XnwInvhZzEQzeYd4Q5UvT9b1UmJddZtgKAOQgKFz63fJPrir6Hkoq08mlsheuAWIKYGtaXX7jc2819DMT0r925tOWit3+jC/+uTsYyoJ+jtycBSdLK6dF715ATHRSfu7xaDX2nS8X5SmydPmmg9WramPtHkCxivCNEjYWu9vO9CADQiJbnFhfcXthd2iGXVJvmhNJM8PX7mOVS4pAMTlIr8Opkv3lefLNi8nuNFMrJ8hA135krw5SRIzGHlHiiB7w4vikn9JnjiLr623qa06jxE9UIe8U/t0jdWpHPgziCVqnnob3nvC+1kuN//fD4ph/fltU4B7PzaVVimUoua5LgMEJUl0qpDLowKjxdU3QHwG1IXc0g2w2ytbzMQS1VVQjBhxW3xVtLVZ+6xp0gJJabokL/MxC2JbxIpLNX2niBG3bpVGwKucTOUHNxLEOjgCz+wRmdbaBnho3IEHvgIWDRaXpZSGeqv+LzZ8bZra2XJQQ30EdhITWehE/0aLjhRE1Hx0fkhMjlB5YK1/lKjj1evqfzJL1ilVoq42O9mUna2LtkNFB4zKMwPaCQaxRHXloBb/6Oe3ASvGigFa7iGil2OnB02z+sjkNluAadYvSaoawMq8I8XAqpraX9WFJNU9w9Giuwhat71rCKBbAR0fuLH93ywqJ8MMb8fESGa2hSJqvqx1c1A7i0D28vGqV1boxnWqx3t393+IrhZ2mhRgEEtki7C+IogtKxBTl47/2TT6vzJfsyC2Lme541aLRvyNfVmn/3QxbWv6QdEXVtmM3xaCu4oglqUERPbpno/Ee2hznyHu70KS7Pr9tBl/WhE1Q+GGulhI4lJ8dQEsYBp9CtStS4JHyA039q4XpUr0SL38V/VTVjYXob1FHbJ5qQYR2Y/Qnjc+qxmRAYNYIluE9QVuf000Hm83tOZ1nb2Bh5aIGrCapntsDhzdmn8AC4g2YZJkOWc8ERH9LUl6vV7f1AfRWPLz8+Hh4YG8vDy4u7vX/gQiIiIialR1jdfYYouIiIiI7A6DWCIiIiKyOwxiiYiIiMjuMIglIiIiIrvDIJaIiIiI7A6DWCIiIiKyOwxiiYiIiMjuMIglIiIiIrvDIJaIiIiI7A6DWCIiIiKyOwxiiYiIiMjuMIglIiIiIrvDIJaIiIiI7A6DWCIiIiKyOwxiiYiIiMjuMIglIiIiIrvDIJaIiIiI7A6DWCIiIiKyOwxiiYiIiMjuMIglIiIiIrtTryB2wYIFiIiIgEajQWxsLHbs2FHj+tu2bUNsbCw0Gg0iIyPxxRdfWDy+ZMkSSJJU5aukpOSG9ktEREREtyabg9iVK1diypQpmDlzJhISEtCvXz8MGzYMqampVtc/f/48hg8fjn79+iEhIQGvvvoqnn/+eaxevdpiPXd3d2RkZFh8aTSaeu+XiIiIiG5dkl6v19vyhF69eqFbt274/PPPjcs6dOiAkSNHYu7cuVXWf/nll7F27VokJSUZl02ePBlHjhzBnj17AIhM7JQpU5Cbm9tg+7UmPz8fHh4eyMvLg7u7e52eQ0RERESNp67xmk2Z2LKyMhw6dAiDBw+2WD548GDs3r3b6nP27NlTZf0hQ4bg4MGDKC8vNy4rKChAWFgYWrRogXvuuQcJCQk3tF8AKC0tRX5+vsUXEREREdk/m4LY7OxsaLVaBAQEWCwPCAhAZmam1edkZmZaXb+iogLZ2dkAgPbt22PJkiVYu3Ytli9fDo1Gg759+yI5Obne+wWAuXPnwsPDw/gVGhpqy8slIiIiomaqXgO7JEmy+F6v11dZVtv65st79+6NcePGISYmBv369cOqVavQtm1b/Pe//72h/c6YMQN5eXnGr7S0tNpfHBERERE1ew62rOzr6wulUlkl+5mVlVUlSyoLDAy0ur6DgwN8fHysPkehUKBHjx7GTGx99gsAjo6OcHR0rPV1EREREZF9sSkTq1arERsbi/j4eIvl8fHx6NOnj9XnxMXFVVl/06ZN6N69O1QqldXn6PV6JCYmIigoqN77JSIiIqJbl02ZWACYNm0axo8fj+7duyMuLg5ffvklUlNTMXnyZADiEn56ejqWLl0KQHQi+PTTTzFt2jQ8+eST2LNnDxYtWoTly5cbtzl79mz07t0bbdq0QX5+Pj755BMkJibis88+q/N+iYiIiOjvw+YgdvTo0cjJycGcOXOQkZGBTp06YcOGDQgLCwMAZGRkWPRujYiIwIYNGzB16lR89tlnCA4OxieffIIHH3zQuE5ubi6eeuopZGZmwsPDA127dsX27dvRs2fPOu+XiIiIiP4+bO4Ta8/YJ5aIiIioebspfWKJiIiIiJoDBrFEREREZHcYxBIRERGR3WEQS0RERER2h0EsEREREdkdBrFEREREZHcYxBIRERGR3WEQS0RERER2h0EsEREREdkdBrFEREREZHcYxBIRERGR3WEQS0RERER2h0EsEREREdkdBrFEREREZHcYxBIRERGR3WEQS0RERER2h0EsEREREdkdBrFEREREZHcYxBIRERGR3WEQS0RERER2h0EsEREREdkdBrFEREREZHcYxBIRERGR3WEQS0RERER2h0EsEREREdkdBrFEREREZHcYxBIRERGR3WEQS0RERER2h0EsEREREdkdBrFEREREZHcYxBIRERGR3WEQS0RERER2h0EsEREREdkdBrFEREREZHcYxBIRERGR3WEQS0RERER2h0EsEREREdmdegWxCxYsQEREBDQaDWJjY7Fjx44a19+2bRtiY2Oh0WgQGRmJL774wuLxr776Cv369YOXlxe8vLwwaNAg7N+/32KdWbNmQZIki6/AwMD6HD4RERER2Tmbg9iVK1diypQpmDlzJhISEtCvXz8MGzYMqampVtc/f/48hg8fjn79+iEhIQGvvvoqnn/+eaxevdq4ztatW/HII4/gzz//xJ49e9CyZUsMHjwY6enpFtvq2LEjMjIyjF/Hjh2z9fCJiIiI6BYg6fV6vS1P6NWrF7p164bPP//cuKxDhw4YOXIk5s6dW2X9l19+GWvXrkVSUpJx2eTJk3HkyBHs2bPH6j60Wi28vLzw6aefYsKECQBEJvbnn39GYmKiLYdrIT8/Hx4eHsjLy4O7u3u9t0NEREREN0dd4zWbMrFlZWU4dOgQBg8ebLF88ODB2L17t9Xn7Nmzp8r6Q4YMwcGDB1FeXm71OUVFRSgvL4e3t7fF8uTkZAQHByMiIgJjxozBuXPnajze0tJS5OfnW3wRERERkf2zKYjNzs6GVqtFQECAxfKAgABkZmZafU5mZqbV9SsqKpCdnW31Oa+88gpCQkIwaNAg47JevXph6dKl2LhxI7766itkZmaiT58+yMnJqfZ4586dCw8PD+NXaGhoXV8qERERETVj9RrYJUmSxfd6vb7KstrWt7YcAN577z0sX74ca9asgUajMS4fNmwYHnzwQURHR2PQoEFYv349AODbb7+tdr8zZsxAXl6e8SstLa32F0dEREREzZ6DLSv7+vpCqVRWybpmZWVVybbKAgMDra7v4OAAHx8fi+UffPAB3nnnHWzevBmdO3eu8VhcXFwQHR2N5OTkatdxdHSEo6NjjdshIiIiIvtjUyZWrVYjNjYW8fHxFsvj4+PRp08fq8+Ji4ursv6mTZvQvXt3qFQq47L3338fb731Fn7//Xd079691mMpLS1FUlISgoKCbHkJRERERHQLsLmcYNq0afj666+xePFiJCUlYerUqUhNTcXkyZMBiEv4ckcBQHQiuHDhAqZNm4akpCQsXrwYixYtwvTp043rvPfee3jttdewePFihIeHIzMzE5mZmSgoKDCuM336dGzbtg3nz5/Hvn37MGrUKOTn5+Oxxx67kddPRERERHbIpnICABg9ejRycnIwZ84cZGRkoFOnTtiwYQPCwsIAABkZGRY9YyMiIrBhwwZMnToVn332GYKDg/HJJ5/gwQcfNK6zYMEClJWVYdSoURb7evPNNzFr1iwAwMWLF/HII48gOzsbfn5+6N27N/bu3WvcLxERERH9fdjcJ9aesU8sERERUfN2U/rEEhERERE1BwxiiYiIiMjuMIglIiIiIrvDIJaIiIiI7A6DWCIiIiKyOwxiiYiIiMjuMIglIiIiIrvDIJaIiIiI7A6DWCIiIiKyOwxiiYiIiMjuMIglIiIiIrvDIJaIiIiI7A6DWCIiIiKyOwxiiYiIiMjuMIglIiIiIrvDIJaIiIiI7A6DWCIiIiKyOwxiiYiIiMjuMIglIiIiIrvDIJaIiIiI7A6DWCIiIiKyOwxiiYiIiMjuMIglIiIiIrvDIJaIiIiI7A6DWCIiIiKyOwxiiYiIiMjuMIglIiIiIrvDIJaIiIiI7A6DWCIiIiKyOwxiiYiIiMjuMIglIiIiIrvDIJaIiIiI7A6DWCIiIiKyOwxiiYiIiMjuMIglIiIiIrvDIJaIiIiI7A6DWCIiIiKyO/UKYhcsWICIiAhoNBrExsZix44dNa6/bds2xMbGQqPRIDIyEl988UWVdVavXo2oqCg4OjoiKioKP/300w3vl4iIiIhuTTYHsStXrsSUKVMwc+ZMJCQkoF+/fhg2bBhSU1Otrn/+/HkMHz4c/fr1Q0JCAl599VU8//zzWL16tXGdPXv2YPTo0Rg/fjyOHDmC8ePH4+GHH8a+ffvqvV8iIiIiunVJer1eb8sTevXqhW7duuHzzz83LuvQoQNGjhyJuXPnVln/5Zdfxtq1a5GUlGRcNnnyZBw5cgR79uwBAIwePRr5+fn47bffjOsMHToUXl5eWL58eb32a83/t3dvIVG8bxzAv5utm0htqbmHDrIEEWQIaYddOhG0JdiBurAuwm4CIyWzm6LC6qYDFBEWUUgUBHbTCZJqI7VEBTEhswihgya7iFa6Ze16eH4X4fKfdtXVfzkz+f3AgvvOOzPv+PWBx2kbu7u7YTab0dXVhWnTpo3msomIiIhoHETbr00ezUGDwSDq6+tx8OBBxbjb7UZ1dXXEfWpqauB2uxVj69evR0lJCXp7e2E0GlFTU4P9+/eHzTl//vyYzwsAgUAAgUAg9L6rqwvAr28OEREREWnPYJ820n3WUTWxHR0d6O/vh8ViUYxbLBb4fL6I+/h8vojz+/r60NHRAZvNNuScwWOO5bwAcPLkSRw/fjxsfM6cOUNfJBERERGpzu/3w2w2D7l9VE3sIIPBoHgvImFjI83/fTyaY472vIcOHUJhYWHo/cDAAD5//ozExMRh9/tTuru7MWfOHLS2tvLjCzrD7PSN+ekXs9MvZqdvWspPROD3+2G324edN6omNikpCTExMWF3P9vb28Pukg6yWq0R50+ePBmJiYnDzhk85ljOCwAmkwkmk0kxNn369KEv8C+ZNm2a6j8QNDbMTt+Yn34xO/1idvqmlfyGuwM7aFRPJ4iNjUV6ejo8Ho9i3OPxwOVyRdzH6XSGzX/8+DEyMjJgNBqHnTN4zLGcl4iIiIj+XaP+OEFhYSF27tyJjIwMOJ1OXLlyBS0tLcjNzQXw65/w29racOPGDQC/nkRQXFyMwsJC7N69GzU1NSgpKQk9dQAA9u3bh1WrVuH06dPYvHkz7t27hydPnqCqqirq8xIRERHRxDHqJjY7OxudnZ04ceIEvF4vUlNTUVZWhpSUFACA1+tVPLvV4XCgrKwM+/fvx8WLF2G323HhwgVs27YtNMflcqG0tBRHjhzB0aNHMW/ePNy6dQvLli2L+rxaZDKZUFRUFPaRBtI+ZqdvzE+/mJ1+MTt902N+o35OLBERERGR2sb0Z2eJiIiIiNTEJpaIiIiIdIdNLBERERHpDptYIiIiItIdNrF/yaVLl+BwODBlyhSkp6fj+fPnai+JfnPs2DEYDAbFy2q1hraLCI4dOwa73Y64uDisWbMGTU1NKq54Ynv27Bk2btwIu90Og8GAu3fvKrZHk1cgEEB+fj6SkpIQHx+PTZs24dOnT+N4FRPTSNnt2rUrrBaXL1+umMPs1HHy5EksWbIEU6dORXJyMrZs2YK3b98q5rD2tCma7PRee2xi/4Jbt26hoKAAhw8fRkNDA1auXInMzEzFo8dIGxYuXAiv1xt6NTY2hradOXMG586dQ3FxMerq6mC1WrFu3Tr4/X4VVzxxff/+HWlpaSguLo64PZq8CgoKcOfOHZSWlqKqqgrfvn1DVlYW+vv7x+syJqSRsgOADRs2KGqxrKxMsZ3ZqaOyshJ79+5FbW0tPB4P+vr64Ha78f3799Ac1p42RZMdoPPaE/rjli5dKrm5uYqxBQsWyMGDB1VaEUVSVFQkaWlpEbcNDAyI1WqVU6dOhcZ+/vwpZrNZLl++PE4rpKEAkDt37oTeR5PX169fxWg0SmlpaWhOW1ubTJo0SR4+fDhua5/ofs9ORCQnJ0c2b9485D7MTjva29sFgFRWVooIa09Pfs9ORP+1xzuxf1gwGER9fT3cbrdi3O12o7q6WqVV0VCam5tht9vhcDiwfft2vHv3DgDw/v17+Hw+RY4mkwmrV69mjhoUTV719fXo7e1VzLHb7UhNTWWmGlBRUYHk5GTMnz8fu3fvRnt7e2gbs9OOrq4uAEBCQgIA1p6e/J7dID3XHpvYP6yjowP9/f2wWCyKcYvFAp/Pp9KqKJJly5bhxo0bePToEa5evQqfzweXy4XOzs5QVsxRH6LJy+fzITY2FjNmzBhyDqkjMzMTN2/exNOnT3H27FnU1dVh7dq1CAQCAJidVogICgsLsWLFCqSmpgJg7elFpOwA/dfeqP/sLEXHYDAo3otI2BipKzMzM/T1okWL4HQ6MW/ePFy/fj30wXbmqC9jyYuZqi87Ozv0dWpqKjIyMpCSkoIHDx5g69atQ+7H7MZXXl4eXr58iaqqqrBtrD1tGyo7vdce78T+YUlJSYiJiQn7DaW9vT3sN1XSlvj4eCxatAjNzc2hpxQwR32IJi+r1YpgMIgvX74MOYe0wWazISUlBc3NzQCYnRbk5+fj/v37KC8vx+zZs0PjrD3tGyq7SPRWe2xi/7DY2Fikp6fD4/Eoxj0eD1wul0qromgEAgG8efMGNpsNDocDVqtVkWMwGERlZSVz1KBo8kpPT4fRaFTM8Xq9ePXqFTPVmM7OTrS2tsJmswFgdmoSEeTl5eH27dt4+vQpHA6HYjtrT7tGyi4S3dWeOv+f7N9WWloqRqNRSkpK5PXr11JQUCDx8fHy4cMHtZdG/+PAgQNSUVEh7969k9raWsnKypKpU6eGcjp16pSYzWa5ffu2NDY2yo4dO8Rms0l3d7fKK5+Y/H6/NDQ0SENDgwCQc+fOSUNDg3z8+FFEossrNzdXZs+eLU+ePJEXL17I2rVrJS0tTfr6+tS6rAlhuOz8fr8cOHBAqqur5f3791JeXi5Op1NmzZrF7DRgz549YjabpaKiQrxeb+jV09MTmsPa06aRsvsXao9N7F9y8eJFSUlJkdjYWFm8eLHikRakDdnZ2WKz2cRoNIrdbpetW7dKU1NTaPvAwIAUFRWJ1WoVk8kkq1atksbGRhVXPLGVl5cLgLBXTk6OiESX148fPyQvL08SEhIkLi5OsrKypKWlRYWrmViGy66np0fcbrfMnDlTjEajzJ07V3JycsJyYXbqiJQbALl27VpoDmtPm0bK7l+oPYOIyPjd9yUiIiIi+v/xM7FEREREpDtsYomIiIhId9jEEhEREZHusIklIiIiIt1hE0tEREREusMmloiIiIh0h00sEREREekOm1giIiIi0h02sURERESkO2xiiYiIiEh32MQSERERke6wiSUiIiIi3fkP4Tcbo0jN8ywAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 5))\n",
    "plt.plot(history.history['mae'], label='train mae')\n",
    "plt.plot(history.history['val_mae'], label='validation mae')\n",
    "plt.ylim([0, 0.2])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a2a59e42-0c0c-4fa3-8ae6-7e41ad608c33",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_prediction(y_pred, y_true):\n",
    "    plt.figure(figsize=(16,4))\n",
    "    plt.plot(np.squeeze(y_pred), label='prediction')\n",
    "    plt.plot(np.squeeze(y_true), label='true')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    print('MAE: %.3f' % mean_absolute_error(np.squeeze(y_pred), np.squeeze(y_true)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a9536504-3ea8-4074-bf9f-f9f0c38716ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 244ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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RERGRFqqkALLLh8PWFCwEiOxpgoXpu6D7Oc23Nml1GhwsBJg1axazZs2q8bl58+ad9Njdd9/N3Xff3ZhbNZzFUu9SYE/z8fGhrKzslMdFRkaydevWao9t3LgRb29vAPr27Yuvry9JSUkqORYRERERERFpazITzdYvDALa13xMRC/gCw05kVNqVLBQXKNz586sXr2a/fv3ExQUVJEheKJJkybx73//mzfffJMxY8bw9ttvs3Xr1ooS4+DgYB544AHuu+8+7HY748ePJycnhxUrVhAUFMSNN97YnF+WiIiIiIiIiDSnqsNNahu8WjEReXfzrElaLY2/8aAHHngAm81G3759iYyMrFa+XdXUqVP585//zEMPPcSIESPIzc3lhhtuqHbMP/7xD/7yl7/wxBNP0KdPH6ZOncoXX3xBly5dmuNLERERERERERFPqWsSslNkebAwXZmFUjeLwy0jiV0rJyeH0NBQsrOzCQkJqfZcYWEh+/bto0uXLvj5+XloheIu+v6KiIiIiIiInMKns2DjfDjrUTjzwZqPKToOT5TPOXhoX+3lynLaqiu+VpUyC0VEREREREREWrOKzMKutR/jGwQhHc3+0V3uX5O0WgoWioiIiIiIiIi0ZvUpQwaVIku9KFgoIiIiIiJyoqRV8MkdkJ/p6ZWIiNQtPxPyM8x++251HxvRy2yVWSh1ULBQRERERETkREuegk3vwJq5nl6JiEjdMhPNNriDKTWuizOzUMFCqYOChSIiIiIiIifKSjLbA8s8uw4RkVPJ2Gu2pypBBohQGbKc2mkTLLTb7Z5egriBvq8iIiIi0uwcDsg+aPaTVkNpsWfXIyJSl4p+hacoQYbKMuSsJCgpcN+apFXz8vQCmsrHxwer1crhw4eJjIzEx8cHi8Xi6WVJEzkcDoqLi0lPT8dqteLj4+PpJYmIiIhIW1FwDEryzH5pARzeAAmjPLsmEZHa1He4CUBgBPi3M7/nMvZAzAD3rk1apVYfLLRarXTp0oWUlBQOHz7s6eWIiwUEBJCQkIDVetokwYqIiIhIS5edXP3z/UsVLBSRlqshwUKLxZQiJ682pcgKFkoNWn2wEEx2YUJCAqWlpZSVlXl6OeIiNpsNLy8vZYqKiIiISPNyliA77V8GZzzgmbWIiNTF4WhYz0KoDBZqyInU4rQIFgJYLBa8vb3x9vb29FJERERERKQ1yyrPLIzoaV5MJ6+GshKw6bWGiLQwuammbYLFBmGd6ndOZHnfQgULpRaq7RQREREREanKWYbc/RwICIeSfNO3UESkpXGWIIclgFc9e/1XTERWsFBqpmChiIiIiIhIVc4y5LAE6DTW7O9f5rn1iIjUpiH9Cp2cwcKMPWBXKzc5mYKFIiIiIiIiVTmDhaEdofMEs69goYi0RI0JFoYlgJcflBVB1gH3rEtaNQULRUREREREqnKWIYfGQ6dxZj9plelbKCLSklQMN+lW/3OstsrgokqRpQYKFoqIiIiIiDiVFsHxI2Y/NB6i+oJ/OzNAIGWTZ9cmInKixmQWQmUp8tGdrl2PnBYULBQREREREXHKOWS2Xv4Q0B6s1srswv1LPbcuEZETlZXCsf1mv6HBQk1EljooWCgiIiIiIuKU5SxB7ggWi9nvPN5s1bdQRFqS7CSwl5j+gyFxDTs3oofZqgxZaqBgoYiIiIiIiFPFJOT4ysecwcKkVSaTR0SkJXD2K2zfzWRBN0SEM7NwJzgcrl2XtHoKFoqIiIiIiDhVnYTsFNUP/MKg+Lj6FopIy1HRr7ABw02cwrsDFijMhrx0ly5LWj8FC0VERERERJyqTkJ2qtq38IBKkUWkhWjscBMAbz9o18nsp2vIiVSnYKGIiIiIiIhTTcFCUN9CEWl5mhIshOqlyCJVKFgoIiIiIiLiVFMZMkBnZ2bhSvUtFJGWwdmzsDFlyACRPc326G7XrEdOGwoWioiIiIiIgGnyX1uwMLo/+IVCcS6kbm7+tYmIVFVSUJkJ3ejMwvJgocqQ5QQKFoqIiIiIiADkHYXSQsACIXHVn7PaKvsWqhRZRDwtM9Fs/UIhILxx16goQ97lmjXJaaNRwcIXXniBLl264Ofnx7Bhw1i6dGmtx950001YLJaTPvr169foRYuIiIiIiLicM0snOAa8fE5+XsFCEWkpqvYrtFgad42IHmabcwiKcl2zLjktNDhY+N5773HvvffyyCOPsGHDBiZMmMB5551HUlJSjcc/++yzpKSkVHwkJyfTvn17rrzyyiYvXkRERERExGVqK0F2cg45SVoJ9rLmWZOISE2aOtwEIKA9BEaaffUtlCoaHCx8+umnmT59OjNmzKBPnz7Mnj2b+Ph45syZU+PxoaGhxMTEVHysXbuWY8eOcfPNNzd58SIiIiIiIi5zqmBhzADwDYWiHPUtFBHPyigvQ25KsBBUiiw1alCwsLi4mHXr1jFlypRqj0+ZMoUVK1bU6xpz587lnHPOoVOnTrUeU1RURE5OTrUPERERERERt3KWIYfG1/y81Qadxpj9/cubZ00iIjWpyCxs5CRkp0gNOZGTNShYePToUcrKyoiOjq72eHR0NKmpqac8PyUlhW+++YYZM2bUedwTTzxBaGhoxUd8fC3/WIuIiIiIiLjKqYKFUFmKrL6FIuJJrihDhsqJyMoslCoaNeDEckLzTIfDcdJjNZk3bx5hYWFceumldR738MMPk52dXfGRnJzcmGWKiIiIiIjU36nKkKFyyMmBFepbKCKeUXAM8o+a/fZNzCxUsFBq4NWQgyMiIrDZbCdlEaalpZ2UbXgih8PBa6+9xvXXX4+PTw2Txarw9fXF19e3IUsTERERERFpmixnZmEdwcKYgeAbAkXZcGQrdBjUPGsTEXFy9isM7gC+QU27VmR5z8LMRCgrAZt3064np4UGZRb6+PgwbNgwFi5cWO3xhQsXMnbs2DrPXbx4MXv27GH69OkNX6WIiIiIiIg7lRRUZuqE1VGGbPOChNFmX6XIIuIJzhLkpmYVAoTEgXcg2Eshc1/TryenhQaXId9///28+uqrvPbaa+zYsYP77ruPpKQkZs6cCZgS4htuuOGk8+bOncuoUaPo379/01ctIiIiIiLiStmHzNYnCPzC6j62om+hhpyIiAe4argJgMUCET3M/lENORGjQWXIANOmTSMjI4PHHnuMlJQU+vfvz9dff10x3TglJYWkpKRq52RnZ/PRRx/x7LPPumbVIiIiIiIirpRdpQT5VP3YncHCA8vBbgdro1rBi4g0jquGmzhF9oKUjWYicp+LXHNNadUaHCwEmDVrFrNmzarxuXnz5p30WGhoKPn5+Y25lYiIiIiIiPvVZxKyU8wg8AmGwqzyvoUD3bo0EZFqXB0srMgs3O2a60mrp7fARERERERE6jMJ2alq38IDKkUWkWbkcEDGXrPvsmBh+ZATlSFLOQULRUREREREGhIshCp9CzXkRESaUW4qlOSBxQrtOrvmms6JyEd3m2CktHkKFoqIiIiIiDSkDBlO7lsoItIcMsuzCsM6gZePa67ZrgtYbFB8HHIOu+aa0qopWCgiIiIiIpJVHiwMq2ewsMMgMzm54BikbXffukREqnJ1v0IwQcf2Xc2+SpEFBQtFRERERKSts9sh55DZr28Zss0b4keZfZUii0hzcUewECpLkdN3ufa60iopWCgiIiIiIm1bXjqUFZseYMEd6n9eRSmygoUi0kwqhpt0c+11KyYiK1goChaKiIiIiEhb5+xXGBxrMgbrq/MEs92vvoUi0kwqMgtdHSx0DjlRsFAULBQRERERkbauYrhJPUuQnWIHg3cgFGRC+g6XL0tEpJqyUsjcZ/ZdXobc02zT1bNQFCwUEREREZG2Lvug2TY0WGjzhgRn38Llrl2TiMiJspPAXgI2Xwhp4O+rU4koDxbmpZnBTdKmKVgoIiIiIiJtW2ODhVDZt3D/UtetR0SkJlX7FVpdHM7xDTatGACO7nbttaXVUbBQRERERETatqzyMuSw+Iaf28k55GQ5OByuW5OIyInc1a/QSaXIUk7BQhERERERadsqehY2IlgYOwS8AyA/A9J/de26RESqqggWurhfoZOGnEg5BQtFRERERKRta0oZspcPxI80+/uXuW5NIiIncnuwsIfZKljY5ilYKCIiIiIibVdxnplmDI3LLIQqfQsVLBQRN8pINFt3BQsjyzMLVYbc5ilYKCIiIiIibZczq9A3FPxCGneNzhPMdv8y9S0UEfcoKahsmeDuMuSsA1BS6J57SKugYKGIiIiIiLRdFf0KG1GC7BQ7FLz8If+oMnJExD0y9wEO8AuFgHD33CMoyrxx4rBD5l733ENaBQULRURERESk7WpKv0Knqn0LD6gUWUTcwNmvsH03sFjccw+LRRORBVCwUERERERE2rKs8szCsEb2K3RS30IRcSd3DzdxqpiIvNu995EWTcFCERERERFpu1yRWQhVgoXL1bdQRFwvo7ws2O3BQudEZGUWtmUKFoqIiIiISNtVESxsYmZh3DDw8oO8NGXkiIjrVWQWdnPvfSomIu9y732kRVOwUERERERE2q7sJLNtarDQyxc6jjD7+5c27VoiIidqtjLk8p6FGbvBXubee0mLpWChiIiIiIi0TfYyyDls9ptahgzQeYLZHlje9GuJiDgVHDPT1sH9mYVhncDmA6WFldPipc1RsFBERERERNqm40fAXgoWGwTHNP16VYecqG+hiLhKRqLZBsWAb7B772XzqsxeVClym6VgoYiIiIiItE3OfoUhcWC1Nf16ccPA5muCkM6SQRGRpmquEmQnZymyhpy0WQoWioiIiIhI25RV3q8wrIn9Cp28/SB+pNnfv8w11xQRyXROQnZzCbJTRbBQmYVtlYKFIiIiIiLSNlVMQnZBv0KnTuPMVsFCEXGV5s4s1ETkNk/BQhERERERaZvcESx09i08sFx9C0XENSqChc2dWbhTv8faKAULRURERESkbXJHsLDjCNO3MDcFMhNdd10RaZscDshwliE3U2ZheHfAUj6FOaN57iktSqOChS+88AJdunTBz8+PYcOGsXTp0jqPLyoq4pFHHqFTp074+vrSrVs3XnvttUYtWERERERExCWyk802NMF11/T2g47Dzf7+ul8niYic0vEjUHwcLFZo17l57ukTUNnLNV1DTtqiBgcL33vvPe69914eeeQRNmzYwIQJEzjvvPNISkqq9ZyrrrqKH3/8kblz57Jz504WLFhA7969m7RwERERERGRJqkIFrowsxAqS5H3L3ftdUWk7XGWIIclgJdv8903orxvoSYit0kNDhY+/fTTTJ8+nRkzZtCnTx9mz55NfHw8c+bMqfH4b7/9lsWLF/P1119zzjnn0LlzZ0aOHMnYsWObvHgREREREZFGKcyBwmyz77Zg4TL1+xKRpmnu4SZOFX0LdzfvfaVFaFCwsLi4mHXr1jFlypRqj0+ZMoUVK1bUeM7nn3/O8OHD+de//kVcXBw9e/bkgQceoKCgoNb7FBUVkZOTU+1DRERERETEZZz9Cv3bgW+Qa6/dcQTYfCD3sPoWikjTeCpYGFkeLFQZcpvUoGDh0aNHKSsrIzo6utrj0dHRpKam1nhOYmIiy5YtY+vWrXzyySfMnj2bDz/8kDvvvLPW+zzxxBOEhoZWfMTHxzdkmSIiIiIiInVzx3ATJ29/iCvvW3hApcgi0gTNPdzEqaIMeVfz3ldahEYNOLFYLNU+dzgcJz3mZLfbsVgszJ8/n5EjR3L++efz9NNPM2/evFqzCx9++GGys7MrPpKTkxuzTBERERERkZpV9Ct0U2JC53Fmu3+Ze64vIm1DRWZht+a9r7MMOTsZivOa997icQ0KFkZERGCz2U7KIkxLSzsp29CpQ4cOxMXFERoaWvFYnz59cDgcHDx4sMZzfH19CQkJqfYhIiIiIuI2hdlgt3t6FdKc3JlZCNWHnKhvoYg0RlkpZO4z+82dWRgYDgHhZl99C9ucBgULfXx8GDZsGAsXLqz2+MKFC2sdWDJu3DgOHz7M8ePHKx7btWsXVquVjh3d9A+ziIiIiEh97VsK/0yApf/x9EqkObk7s7DjSLB6Q85BOLbfPfcQkdNbdjLYS8DmCyEeiJ+oFLnNanAZ8v3338+rr77Ka6+9xo4dO7jvvvtISkpi5syZgCkhvuGGGyqOv/baawkPD+fmm29m+/btLFmyhAcffJBbbrkFf39/130lIiIiIiKNsfdHs90437PrkObl7sxCnwCIG2b2VYosIo3h7FfYvitYG9VFrmkiepitgoVtToN/2qZNm8bs2bN57LHHGDx4MEuWLOHrr7+mU6dOAKSkpJCUlFRxfFBQEAsXLiQrK4vhw4dz3XXXcdFFF/Hcc8+57qsQEREREWksZ4nXsX3KAGtLKoKFbhym6CxF1pATEWkMT/UrdIoszyzUROQ2x6sxJ82aNYtZs2bV+Ny8efNOeqx3794nlS6LiIiIiLQImYmV+4mLYNhNnlqJNJeyUsg5bPbD3BksHAdLnzKZhQ4H1DIUUkSkRhXBwmbuV+ikMuQ2ywN5rCIiIiIiLYTDUT2bMHGRp1YizSk3BRxlpqdgYJT77hM/Cqxepu9Y1gH33UdETk8eDxaWlyFn7DVvskiboWChiIiIiLRd+RlQlFP5eeJiTUVuCypKkOPc2wfMJ7BK30KVIotIAzl7FnoqWBgaD94BZsiK2nS0KQoWioiIiEjb5exXGBQNPkFQkAmpmz27JnG/5uhX6NRpnNlqyImINERJQeXUdk8FC63WynsfVd/CtkTBQhERERFpu46VBwsjelYOo0j82XPraS0Ks6Egy9OraLzs8oGMzREsrBhyomChyGmprBQ+vwdWPG9aW7hK5j7AAb6hEBjhuus2VERPs1XfwjZFwUIRERERabucw03adYauZ5l99S2sW3EevDge/jcKivM9vZrGqcgs7Oj+ezn7FmYlwTH1LRQ57exbDOvfgO8fgYV/cV3AsOokZE8OR6qYiKxgYVuiYKGIiIiItF3lZcgb8tqTFjnaPHZgpSn/kpqtf8sEvo6nwpFtnl5N4zRnsNA3CGKHmP1PZ8HSp2HfEijKdf+9RcT9Dq6p3F/xHCz8s2sChp4ebuJUkVmoMuS2xMvTCxARERER8ZjyMuRXtjpYv+8oywJj8MpLhaRV0O0sDy+uBSorgZXPV35+ZAvEj/Dcehorq7wPWFgzlCED9L7ABBQOLKtSjmyByN7QcZgZghI3HKL6gk0v0URaleRfzLbTODiwHFb81wQLpzzetIxATw83cXJmFh7dbb4uT2Y5SrPRv0QiIiIi0mY5MhOxAAcc0aTmFvF9QG/OJ9WUIitYeLKtH1U23AdI3eq5tTSWw1H5NTRHz0KAcfdCwlg4+AscWgcH15m+iek7zMeGt81xXv4QO9gEDzsON9vQeL04F2mp7HY4uNbsT/1/cGgtfPX7yjdVmhIwzHQGC7s1fZ1N0b4rWKxQlAO5qRDSwbPrkWahYKGIiIiItE1FuVjy0gHI8o2jW3Ag32f05XyfRRTv+hGfyX/38AJbGLsdls02+zEDIHULHGmFwcLCbCg+bvZD4prnnhYLJIwyH07H08oDh2tNgOHQevNiPGml+XAKjCoPHA412YdxQ8EvtHnWLSJ1O7oLirLBOwCi+5tgPxb46n4TMHQ4YOr/NS5gWLVnoSd5+UK7LiZ4eXSngoVthIKFIiIiItI2lfcrzHAEM2V4T+44sxuzXsqEPPBK38qhQ8nExTVT5llrsPt7kwXnEwznPwWvTYUj200Q0dqKWqE7+xUGhINPgOfWERQFvc4zH2D+HDP2mMDhwbUmkHhkK+Slwc6vzYdTZG/zPegywTNrFxEjebXZxg6tbCEwYroJDn55H6z6H+AwWYf1DBgeLyrFqzgHv/I3s2jv4WAhmFLkzL2mFLnrRE+vRpqBgoUiIiIi0iZlH9pFKJDkiOaakQlEhfjxv9vPZ9/sBLrYk3hp3jxuv/MB4sL8Pb3UlmH5bLMdfrMpj7X5QHEuZB2A9l08urQGae4S5PqyWiGyp/kYfK15rKQAUjabwKEziJh1ANJ/haUKFop43MHyfoXxI6s/PvwWwAJf3gurXjAZhuc+UWvAMCkjn++3p7Jw+xHWHjjGCO99vGuBsoAobH4hbv0S6iWiB+wE0jXkpK1QsFBERERE2qRt2zYxFsgNiGdIdDAA0SF+5A06Fza8TO+CdVz98kreu20MsW09YJi0ypTG2nxg9CyweZvsttTNJvutVQULm3ESclN5+59cvpyyGV6aAPuXm4nKvsGeW59IW5dcPgn5xGAhmDdWLBb44neweg7ggHP/CRYLdruDLYeyWbj9CAu3H2HnkerT0aNLDoIPrDvennff28itZ3SlTwcPBg0jnENOFCxsKxQsFBEREZE2x253cDRpBwARCb2rPRfY5xzY8DITvbfzp8wCrn55Fe/dPpoOoW04YOjsVTjo6sp+VTEDTLAwdSv0uchjS2uwlppZWF8dBpqBA5mJsPdn6Huxp1ck0jYVHKsMnnWsZSr8sJsAC3xxD6x+kYPH8pjjdxs//JrGkZyiisNsVgsjO7dnct9ozukTTdlPK2Ab7LV34OMNh/h4wyHO6BnJbRO6Mq57OJbmHnrknIicvqt57yseo2ChiIiIiLQ5y/YcJbz4MNige68B1Z/sNA6sXsTajzA6LJtVmXDNy6t497YxxIT6eWbBnnRkO+z6BrDA2N9VPh7dv/z5VjbkxJlZGNZKg4UAPaaaTKXd3ylYKOIpB9eZbfuuEBhR4yHZ+SX8ZD2bvKgHuPbIf+i46y16lB5hfumNBPp4cWavSCb3jeasXlGEBfhUnmhJAWDi2NFckNWBb7aksGRXOkt2pdO3Qwi3ndGVCwZ2wNvWTP1iI3qY7fFUMyRKQ5ZOewoWioiIiEib8+6aJP5kPQKAT2T36k/6BkHHkZC0gjljc7l4VQz7M/K55pVVvHvbaKJD2ljAcMVzZtvnQoio8mcV00qDhVnOzMJWUIZcm55TyoOFC1vfgBmR04WzX2HH6iXIyZn5FeXFv+zPpMzuAIayyXYrT3q/wk1e3zO5TxQRVz2Lr3ctIZnyScgdug7gf72HkpyZz9xl+3hvTTLbU3K4972N/Pu7ndw8rjNXj0wgyNfNoR2/UAjpCDkH4fN74LKXwLuN/VvYxuhfFRERERFpU9Jzi1i07SCxZJgHauq31+0sANqlLGPBraOJC/Nn39E8rnllFWk5hc24Wg/LSoYtH5j9cfdVf86ZWXhsPxTmNOuymqQ19SysTadx4BMEx49AykZPr0akbUp2DjcZwZ604zz9/U7Onb2ECf/6mce+3M7KxAzK7A56RQdz11nd+e3MR+Di/wIW4na/je/3fzCDT07kcEDGXrMfbt6giW8fwN8u7seKP07i95N7EhHkw6GsAh7/agdjnviRf37zK0fc/W/TuU+A1Ru2fwpvXwEFWe69n3iUgoUiIiIi0qZ8uO4gMY40rBaHCbgERp58UNeJZrtvCR1DfXn3NhMwTEwvDxjmtpGA4cr/gb0UOk+AjsOqPxfQHoJjzX7a9uZfW2OUlUCuKe9rtT0LAbx8K39Gd3/v0aWItEl2u5lSDhwI6M+F/13Kcz/t4dfUXKwWGNWlPY9e0IfFD07ku/vO4IGpvRgUH4Z16PVwyf8AC6x5Fb76vblWVcePQPFxsFihXedqT7UL9OHus3uw7A+TeOLyAXSNDCS3sJQXF+9l/JM/8eAHm9h1wrAUl+l7Mfz2Q/AJhgPL4PXzIPuQe+7lKQVZpsxaFCwUERERkbbDbnfw7pokOllMCTLtuphplSeKHQq+IVCYBSmbiG8fwIJbRxMb6sfe9Dyue2U16blFJ593OsnPhPVvmP3x99V8jLMUOXVL86ypqXIOAQ6w+dYcJG5Nek41213feXYdIm1R+q9QlAPegby43YfCEjt9O4TwnysHse7Rybx3+xhmTOhKp/DAk88dch1c+gJggbVz4esTAoblJciEJZg3Bmrg523jmpEJ/HDfmbxyw3BGdG5HSZmDD9YdZMozS7j59V9YuTcDR02Zi03RdSLc/DUERZs3ieZOhrQdrr2HJ214C57sAt894umVeJx6FoqIiIhIm7EqMYMDGfmc65tuHmjfueYDbV4mm27nV5D4M8QNJSE8gAW3jWbaS6vYnXac615dxTu3jiYiqOYXc63eLy9DST7EDIRuk2o+Jrq/yWxrLX0Lq5YgN/c0UVfrMcVsD6+H42kQFOXZ9Yi0JeX9CotjhvDRJvPm0z8u7cewTu3rd/7gawELfHoHrH3NlB5f8LTpP+osQW7f7ZSXsVotTO4bzeS+0axPOsYrSxL5dlsqP+9M5+ed6QzsGMrFg2Lx97HhY7Pi42XF18uKd/m+8zFvm3ncx6vycW/n8zYrVmuV35cdBsL0haYUOWM3vDYVrnkXOo1tyJ9gy7TnB3CUte42FS6iYKGIiIiItBnv/JIEwJmRx+EoZoplbbpOLA8WLoIJvwegU3gg7942mmkvr2TXkeNc98pq3rl1FOGnW8CwOA9Wv2T2x99be2Atup/ZHtnWLMtqstOhX6FTcAx0GGx6Fu5eaLKVRKR5JK8BYKOjB8WldgZ1DGVoQruGXWPwNeZ36yczYd3rgAMueKYyszC8e52nn2hoQjvm/HYY+4/m8eqyRD5Ye5DNB7PZfLDpZbXeNgveNisd2/lzz9k9uGBAApbp38OCqyF5Nbx5KVz+MvS7tMn38pjiPDiwwux3O9uza2kBVIYsIiIiIm1CxvEivtuWCsAA/0zzYLsahps4lQ85IWkVFOdXPNw5IpAFt44mKtiXnUdyue7V1WTmFbtr2Z6x4W0oyDT9svpcUvtxMQPM9sj2k/tutUTZ5ZOQw1pxv8KqKkqRv/XsOkTamvLMwncOxwBwy/guWBqTrTzoajNZ2GKFdfPgy3vh6G7zXAODhU6dIwJ5/NIBFcNQLhjYgcl9ozmzZyRju4UzvFM7BnYMpXdMMF0jA+nYzp+oYF/aBXgT6GPD23by11FS5iC/uIxdR45z1zsbuGLOCtYftcANn0GvC6CsCD64CVa/3Kg1twj7l0NZMYQmQEQPT6/G45RZKCIiIiJtwkfrD1JS5mBgx1CC88uDRjVNQnYK7w4hcabPXdJK6F6ZadA1MogFt43m6pdX8WuqCRi+M2MU7QJ93PxVNIOyEljxX7M/9h5Tkl2b9t3Ayw9K8uDYPgg/ddmcR2WVf99b83CTqnpMhcVPwt6fobQYvE6Dnz+Rli4/E47uAmBxfic6hPpx/oAOjb/eoGnlGYa3V/aJhSb/Pg0P8uXusxsX9LLbHZTY7RSXln+U2SkqsfPZxsO8uHgv65OyuPyFFVw0KJaHJr9EfPBfTDn1Nw9C7mE4+6+tr9XDnh/MtvvZrW/tbqDMQhERERE57TkcDt79xQSKrh0RB1kHzBN1lSFbLNC1PLsw8eeTnu4WGcSC8p6FO1Jy+O3c1WTlnwYZhls/Nhl4gZHlfbXqYPOCqD5mvzUMOTmdypABYoeY71Nxrgloi4j7lU9BPmiN5Rgh3DCmM962JoZWBl4Fl71sMgydGplZ6ApWqwVfLxvBft6EB/nSIdSfzhGB/O6cHix6cCJXDe+IxQJfbDrM2bOX8aTtNorO+JM5edkzprS6tJX9e1g1WCgKFoqIiIjI6W/1vkwSj+YR6GPjoi6YUiOrt8kcrEvXiWabuKjGp7tHBbHg1lFEBPmw7bAJGGbnl7hy6S5RUmZnZ2oux05VLu1wwPLZZn/UTPD2P/XFo8snIreGvoWnW7DQaq0cdLL7e8+uRaStSDYlyKtLuuHvbePakQmuue7AKysDhoGRLfb3VHSIH//6zSC+vHs8Y7uFU1xqZ87iRMYuH8qK/o/hsNhg87uwYBoU5Xp6ufWTuQ8y94LVC7qc4enVtAgKFoqIiIjIaW9B+WCTiwfHEXjc7NOuM1htdZ/Y9UyzTd0CeUdrPKRHdDDv3Dqa8EAfth4yAcPkzHzsdkfTFu1wwIb5FS9M6yu/uJR1B47x1sr9/PGjzVz032X0+8t3TJ29hPOfW8rxotLaT979PaRtB59gGDGjfjesCBa28InIDkdlz8LTpQwZKoOF6lso0jySVwOw3t6D3wzrSGiAt+uuPfBKmLkcpn9/6n+fPKxfbCjzZ4zi1RuG0zUykIy8Yq5d251H/B6lzOYPe3+C18+H3COeXuqp7f3RbONHgV+oZ9fSQqhnoYiIiIic1o7lFfPNFjPY5JqR8ZC61jxRV79Cp6AoEww7stVkFw74TY2H9YwOZv6to7j2ldVsOZTNhH/9jK+XlU7hASS0D6RTeED5fgCdwk1D+VOWre38Bj6bBTYfuPY96DbppEOy8ovZdjiHrYey2XY4h22Hs9l3NI/a4pQp2YW8tHgvv5/Sq+YDlj1jtsNvAv+wutfnFFMeLExt4cHCgmNQUj6o5lQZpa1Jt7NMNkzGHsjY2/L7Roq0ZvYy7AfXYgXW2XvywrjOrr9HdF/XX9NNLBYL5/SN5sxekbyzOonZP+zinWO92Gr5E2/7P0VI6maYew789hOI8FxZ9SntKQ8WqgS5goKFIiIiInJa+3jDIYrL7PSLDWFAXCjs2GeeqGsSclVdJ54yWAjQOyaE+TNG8cAHm/g1NZeiUju7jhxn15HjJx1rtUBcO386tQ8kITyATu0DqgUWA329YOXz5uCyYhzvXsfRy95jI73YdtgEBrcfzuFQVkGNa4kM9qVfbEj5Ryj9YkPYkZLDzLfX88rSRK4dlUCH0BNKjJNWm753Nh8YfWf9/mwAovuZbXYSFGTVP8jY3JxZhYFR4O3n2bW4kl8oJIyB/UtNZmj4HZ5ekcjpK20H1pI8ch3+dOw5hK6RQZ5eUYvgbbNy49jOXDokjv/9vIfXl1u4qOCvvOHzJJ2zkrC/Ohnrde9D/AhPL/VkpcWwb4nZ76ZgoVOjgoUvvPAC//73v0lJSaFfv37Mnj2bCRMm1HjsokWLOOuss056fMeOHfTu3bsxtxcRERERqReHw1FRgnzNyAQsFovpTQR1DzepqutZJnCXuMiUstYxJbFPhxC+umcCJWV2Dh0r4EBmPkkZeRzIyC/fz+dAZh6FJXaSMwtIziyAPSdfZ0JgEm+VLacMG7t8+9GnaDO+701jdvGjbHN0rnZsQvsA+seZoGDf8gBhVPDJwbCE9gGM7NyeX/Zn8u/vdvL0VYOrH+DsVThwGoQ0YLKnfztT1pudbPoWdh5X/3Obk7NfYVjrLUEuKi3j0LECsgtK6B8XWpmd2vNcEyzc9R2MVrBQxF0KElfiD2yyd+XmCS04U85DQv29+dP5fbhuVAJPfvsrV2z5G6/5/JtBhYmUvH4Bjitex6ffBZ5eZnXJq6H4uOkTGTPQ06tpMRocLHzvvfe49957eeGFFxg3bhwvvfQS5513Htu3bychofbGnjt37iQkJKTi88jIyMatWERERESkntYeOMaetOP4e9u4ZHCsebAiWFjPzMJOY0y2XXYyZCbWq8zT22alc0QgnSMCger/73U4HKTlFpkAYkYeSZn5FfsHMvPJyi/hiuLPwQaflY3hT9nTecPnSUZZf2W+35O82PW/RHYZSL/YEPrGhhDiV79+WRaLhUcv7MPFzy/n4/WHuGVcF/rHlfdmStsBO78GLDDud/X7c6kqun/LDxZmOfsVtsyhAWB+No4eLyYpM5/kzHySyj+Syz9ScgpxlJeYD+wYyou/HUZsmD/0nArfPwL7l5mBAr7Bnv1CRE5TSZsX0wtICujPNd3CPb2cFqtTeCAvXDeMNfu78P++iOSOtH8wkU2UffBbNuz6K4Mu+R1Wa+1vvDUr5xTkbmeboVECNCJY+PTTTzN9+nRmzDANj2fPns13333HnDlzeOKJJ2o9LyoqirCwsEYvVERERESkoRasNlmFFw3qQLCft8kMPNbAMmSfQNP0fP9SSPy5yT3hLBYL0SF+RIf4MbJL+5Oezzmyn+AXfwEHlI2cxV+iBhAY+QH2739LWOom/pj+MFz4DbRr+AvVgR3DuHRwLJ9uPMzjX21nwa2jTbbl8ufMAX0uhIgeDf+iovvBrm/gyJaGn9tcWshwk4LiMg4eqwwEmmBgQUVwsKCkrM7zA3zM0IPNB7O5+PllzPntMEZ06m5+no/tMxmwfS5qhq9EpG0pKbPjd2QdAB0Hnml+d0qdRnRuz4I7z+HLjT346qv7uKDsJ4Zs+isLdu2k+1WPM6JLCwi4ql9hjRoULCwuLmbdunX88Y9/rPb4lClTWLFiRZ3nDhkyhMLCQvr27cujjz5aY2myU1FREUVFRRWf5+TkNGSZIiIiIiJk55fw1ZYUwJQgA2aicfFxwALtOtX/Yl3PNMHCvT/Xf0pwI4VsmguOUug8gSsvqlKudf0nMO98SP8V3rwEbvkWgmMafP0Hz+3NN1tTWZWYyQ870pgcWwxb3jdPjruvcYtuDUNOnGXIzZxZ6HA4+HzTYeavTmL/0TzScovqPN5igdhQf+Lb+xPfzgzFSQgPIL692Q8P9OHgsQJue2sdO1JyuOblVfzt4n78tudUWP2iKUVWsNCjyuwOcgtLyMovIbughKyCErLyi8kpOPGxEgJ8bFw9Ip4x3cIVfGrhfly3g3MdhwEYOWGqh1fTelitFi4e2pnC/h+w9u2HGJ40l2sK3uG911LwufU1BiWc/KZZs8lNLX+Ty1LjELG2rEHBwqNHj1JWVkZ0dHS1x6Ojo0lNTa3xnA4dOvDyyy8zbNgwioqKeOuttzj77LNZtGgRZ5xxRo3nPPHEE/z9739vyNJERERERKr5ZMNBikrt9I4JZnB8mHkwM9FsQzuCl2/9L9Z1Evz0OOxbCvYysNpcvl7AlJCue8Psj7mr+nOB4XD9p/DaVJNB9tZlcNNXENCwF1pxYf5MH9+FFxbt5YmvdzCp37fY7CY4ScdhjVt39ACzTdvh3j+fpvBAZuGetFz+/Ok2ViZmVHs82NeLhPLp2AntTSDQGQyMDfPD16vuP7/49gF8dMcYHvpwM19uTuHRT7dS3KcntwDsXgh2u8rp3GT74RyW7UmvFvRzBgGzCorJzi8ht6i0oly8Pj7fdJi+HUKYMaELFw6MxcdL37uWaM2y7zkXyPTvRPuQCE8vp9Xx8/Fi+C1Pk7u0J4E/Psw028988PPbDLrxHs8tau9PZhs7GAL1Pa2qUQNOTnzHw+Fw1PouSK9evejVq1fF52PGjCE5OZmnnnqq1mDhww8/zP3331/xeU5ODvHxrbcRsYiIiIg0LzPYxASHKgabQJUS5M4Nu2DsYDN1tjAbDm+AjsNdttZqNsyHomwI7w49ppz8fEgHuOEzeP08SNsOb18BN37e4B51d0zsxntrksk8mop97evYAMbf2/h1t+8C3gFQkg8ZeyGyZ+Ov5S7NmFlYUFzGf3/azStLEykpc+DrZWXWxO5M6h1FfHt/Qv29m5xFFuDjxX+vGUK/2FD+9d2v/HNHONf4++F/PBVSN0HsEBd9NeKUU1jC1S+vJKewtF7HB/rYCAvwIdTfm1B/b8ICzDY0wJswf/P4r6k5fLD2INtTcrj//U08+e2v3Di2M9eN7ERoQP36kYr7rTtwjLDMjeAFAV3HeHo5rVrwhJnsO7iHLjtfIebA5zgcd3suq9bZr7D7OZ65fwvWoGBhREQENpvtpCzCtLS0k7IN6zJ69GjefvvtWp/39fXF17cB7/SKiIiIiFSxPimLnUdy8fWycumQuMonnJmF9Z2E7GS1QZczYMcXpm+hO4KF9jJY9YLZHz2r9syw9l1MhuHr58Hh9fDO1fDbD8Hbv963Cvbz5t7JPUn/4jG8ywopixqArVsT+jVZbRDVBw6tgyNbW16wsLQIjh8x+27OLFy4/Qh/+3wbh7IKAJjUO4q/X9yP+PYBLr+XxWLhjond6NMhmHsWbGBJaX+m2taSsuYzOlyiYKGrzV+VRE5hKbGhfkzpF1MR/KsIAvr7VNn3rpxWfQr3T+7J/NVJvLFiP0dyivjXtzv57497uGp4R24Z34VO4YFu/srkVF5bto9rLbsB8Osy2sOraf1izrgJdr7CqLL17NqfRK8uDWgL4ir2MtNaBMxwE6mmQfnNPj4+DBs2jIULF1Z7fOHChYwdO7be19mwYQMdOnRoyK1FREREROrt3V/MYJMLB8YS6l8lO6ehk5Cr6jrRbPcuatLaavXrV5B1APzbw6Br6j42qjdc/zH4hsCBZfD+jVBa3KDbXTOoPdN9vgPgi5CrTLO8pogu71t4pAX2Lcw5ZLbeAQ0u266v5Mx8ps9bw61vruVQVgFxYf68fP0w5t443C2Bwqom9oris7vGsy3QBDHS1n3O+2uT3XrPtqawpIzXlpvfH/dP6cXfLu7Hvef05OZxXbhsSEcm9Y5mWKd2dIsMIiLIt96BQoCwAB/uPKs7y/4wif9cOYjeMcEUlJTxxsoDTHxqETPfWsfa/Zk4GlLb3EDZ+SV8vy2Vv3+xjXNnL6HfX75l/uoDbrtfa3LwWD7fbT3EIOte80D8SM8u6DTgH9efg95d8LGUcXDF+55ZxOGNUJAJvqHQcYRn1tCCNbgM+f777+f6669n+PDhjBkzhpdffpmkpCRmzpwJmBLiQ4cO8eabbwJmWnLnzp3p168fxcXFvP3223z00Ud89NFHrv1KREREREQwpYJfbDZN6K8ddUIWmbMMuaGZhQBdywf0Ja+G4jwzJdmVVj5vtiOmg089gkuxQ+Da9+Cty2H3d/DJbXDF3Hr3C/Ta/A6hjlwO2KN4eEdXhmXmNy2oFVPet7AlDjnJcvYr7Nj0oOgJikrLeGVJIv/9aQ9FpXa8bRZundCVuyZ1J8CnUV2fGqVLRCC3z5gJzz/PAEsi0z9cyvbDg3jkgj4NClxJzT5ef4j03CJiQ/24eFCsW+7h42XlimEduXxoHMv3ZPDqskQW7Uzn222pfLstlcHxYdw6oStT+0Xj1cTvaU5hCb8kZrIqMYOViRlsT8k5qc/iI59s5Uh2IfdN7tmmh6+8sWI/PUgmyFIIPsEQ2dvTSzotZHS9mI47nyVy/+fAg82/AGcJctczwdZ8v6tbiwb/iUybNo2MjAwee+wxUlJS6N+/P19//TWdOpm00ZSUFJKSkiqOLy4u5oEHHuDQoUP4+/vTr18/vvrqK84//3zXfRUiIiIiIuU+23CIwhI7PaKCGJrQrvqTzjLkdo3ILGzfFUITIDsJDqyEHi7scZS8xgQhbT4w4tb6n9dpLFz9tilF3vYJ+ATBxf89dUCsrARWmODkj+2voiDVwr++28l/r2lC6WpLzix0U7/CpbvT+etn20g8mgfA2G7hPHZJf7pHBbn0PvUVGBGPo8MgrCmbmGjbyLwVofyamsP/rh1KeJDaPDVWmd3By0tMVtn0CV3dPoDEYrEwvkcE43tEsOtILnOX7uOTjYfYmJzFne+sp2M7f24e14VpI+IJ8q3fS/rjRaWs2VcZHNx6KBv7CcHBbpGBjO4azphu4fyaksvzP+/huZ/2cCSniP+7rH+TA5St0fGiUt79JZmLraYEmY7DWuYAp1ao44TrYeez9C/eQtqh/UTFdW7eBahfYZ0aFT6dNWsWs2bNqvG5efPmVfv8oYce4qGHHmrMbUREREREGsThcDB/tXnjutpgE4DCHMgvn0rbmDJki8VkIGx4y/QtdGWwcNX/zHbAlRBc/17ggHmh85u58MFNZm2+ITD1/+oOGG792AQ9AyMZffk9WOas5YtNh7llXGeGnBhgra/ovmabcwjyM91W7tsoLg4WpmYX8vhX2/lycwoAkcG+PHpBHy4eFOvxDCxLj6mQsokHuuznm6RJrErM5OLnl/PS9cPoHxfq0bW1Vt9uTWV/Rj5hAd5cPaJ5B2/2jA7myd8M5IGpvXhr1QHeXnWAg8cK+MeX25m9cBfXjErgprGdiQ2r3rM0v7iUNfuPmeDg3gy2HMqm7IToYJcIExwc3bU9Y7qGExXiV/HchQMhNsyfRz/dwntrk0k/XsTz1w5p1mzZluDDtcnkFpVyRtA+KAU6qgTZVcI79uBX7z70LtlB0tK3ibr60ea7ecExOLTW7HdXv8KatK2/6SIiIiJyWtt0MJtfU3Px8bJy+dC46k86S5ADIxs8PbhCt7PKg4WLmrTOao4dgO2fmf3RNb8hf0p9L4GLn4fPZpnAo18ITPxjzcc6HLD8WbM/aiZ9E6K5YmhHPlx3kMe/2sGHM8c0LuDlFwphCZCVBEe2QZcJjfta3CHbWYac0KTLlJbZmbdiP88s3EVecRlWC9w4tjP3Te5JiF8LmVzbcyos+Rcx6Sv5dOaL3PbOZvYdzeM3L67gySsGcsnguFNfQyo4HA7mLN4DwA1jOhNYz0w+V4sM9uX+yT2ZNbEbH68/xKvLEklMz+PlJYnMXbaPCwZ04Lz+MWw7nMPKxAw2JWdRekJwMKF9AGPKMwdHdw0nJtSvlrsZ145KIDLYl7veWc9Pv6ZxzSuree3G4W0mS7XM7uD1FfsBGOOz1wQL1a/QpdI7XUTvPTton/g50IzBwsRF4LCbknIXZ5yfLhQsFBEREZHThnOwyQUDOhAW4FP9yaaUIDt1OdNsj2yF42kQFNX4azmtfsm8aOk6EWL6N/46Q66D4uPwzUOw6AkTEB1z58nH7V4IadtMyfKI6QA8MKUXX21OYd2BY3y7NZXzBjRyGGH0gPJg4dYWGixs/IvCtfszefTTrfyamgvAkIQwHr+0P/1iW1i2XuxQCIiA/KP0KNrKp3eO43fvbmDRznR+9+5Gth/O4aFze2Oztt0edA2xfE8GWw/l4Odt5aaxnT29HPy8bVw7KoGrR8SzaFcaryzZx8rEDD7fdJjPNx2udmxcmD9juoUzpms4o7uFExdW/4npTpP7RvPOraOY/sZaNiVn8ZsXV/LmLSPdPrSnJfhxxxEOZOTT2S+fkPzyVmsdh3t2UaeZjuOvpXT3U3Qt3kleyk4CO/RqnhurBPmU2l7TARERERE5LeUWllS8WK6xVLApk5CdAiMgZqDZT1zc+Os4FWbDejMYkDF3N/16o26HSeXZGd/9qfLaVS17xmyH3wz+puQ4JtSPW88wQ1/++e2vFJfaG3d/Z7CzpQ05aUIZcsbxIh78YBO/eXElv6bmEhbgzZNXDOCjmWNbXqAQwGqFHlPM/q7vCPX3Zu6NI5g1sRsALy1J5KbXfyErv2HTs9sqZ1bh1SMSaB/oA2Wl5o0CN04mrg+r1cKk3tEsuG00X949nsuHxNEjKojLh8Txr98MZOlDZ7H8j5N46spBXDGsY6MChU7DOrXnw5ljiQvzZ9/RPC57YQVbD2W78KtpmeYuM/9m3NUryzwQ0bPid6a4RudOndngZf5NPbjk7ea5qcMBe34y+90mNc89WyEFC0VERETktPD5psPkF5fRNTKQkV1q6JfnzCxszCTkqrpONFtXlCKvfwuKc00plKv6Jk14AMbeY/Y/vwe2flT5XNJqSFoBVu+TSp5vP6MrkcG+HMjI582V+xt374ohJ1sad747OByNChba7Q7eWZ3EpP8s5oN15vyrR8Tz0+8nMm1EAtaWnJnXszJYCGCzWnjo3N48f+0Q/L1tLN19lIufX87O8ixJqdnmg1ks35OBzWphxoTyNxlemwpP9YAn4uHls+Dj22HJU7DjC0jfCaXNH4TtHxfK09MGs/D+M3l62mCuGh7v8sy/7lFBfDxrLL1jgjl6vIhpL61k6e50l96jJdl6KJvV+zLxslo4N6Q8q1AlyC5nsVhITbgIgLC9nzZPED5tB+QeBi9/6DTO/fdrpRQsFBEREZHTwoLyEuRrTxxs4nRsv9k2pQwZTN9CMENOmvLCpqwUVr9o9kfPOvUE4/qyWGDyYzD8FsABH99WETRi+WyzHXQ1hMRWOy3Q14sHpvQE4L8/7Wlc5ll0P7NN+9V8fS1B3lEoLQQsEFK/fn15RaVMf2MNf/pkC9kFJfTtEMJHd4zln1cMNNllLV23SWD1gozdlUFy4MKBsXx0x1g6tvMnKTOfy15YzrdbUzy40JbtxcVmAvIlg2Lp2C4Asg9VDkUozoXD62Hzu/DTP+C938L/RsL/xcB/h8GCa2DhX2DD25D8ixmo0MpFh/jx/swxjOkaTl5xGTe/voZPNxzy9LLc4rXyrMLzB3QgKH2DeVDDTdwidsyVFDm8iS5OouTwZvff0FmC3Hk8eNfdt7MtU7BQRERERFq9LQez2XooBx+blcuH1pI9VlGG3MTMwoQxYPM1U38z9jT+Ojs+N730AiJg4FVNW9OJLBY4/z9murK9FN6/Ada8Cju/Biww7nc1nvabYfH0jgkmu6CE535sxNfWrovphVhW1LQ/G1dy9isMjgGvUwf60nILmfbySn7emY6ft5W/XtSXz+8ax7BOraj80C/U/JwC7Pq+2lN9Y0P44q7xjOseTn5xGTPfXs9L5UExqZSYfpxvtqYCcPuZpoSbg7+YbfQAmLUarnoLJv0ZBl5tekX6BIOjzPzs7/zaDBL67E6YOxme7Az/7g6vnw9f3AsrX4DdP5iS5lYkxM+bebeM4KJBsZTaHdz73kZeWrwXh4fLsl0pLaeQLzablhbTx8bDoXXmCWUWusXg7gkstQwDIG35W+6/ofoV1osGnIiIiIhIq7dgjckqnNo/pubMr5JCE9yDpvUsBPD2h4RRsG8J7P0ZIno0/BoOB6x83uyPmGGu6WpWK1w6B4rzTODiq9+bx3tfUOuabVYLfzq/Dze89gtvrdrPDWM60TkisGH3jOprgipHtkJUbxd8IU3UgBLkPWnHuen1Xzh4rIDwQB/m3jSCwfFh7l2fu/ScCvuXwu7vYPTMak+1C/ThjZtH8sQ3vzJ32T6e+OZXokJ8uWyIpoI6vbI0EYcDzu4dRa+Y8unpyeXBwoTR5mf7xJ9vhwNyU+HoTji6G47uKv/YbX7/5KWbjwPLK8+x+cCtPzdtuFEz8/Wy8ey0wUQH+/Jq+c9Pak4hf76gb8suz6+nt1YdoKTMwfBO7RjkcwhK8sE3FCKaafhGG2OzWjgUfz4kryJo9+dg/5f5t8QdivMgaaXZd1Xrj9OUMgtFREREpFXLKyrls/JSuGtG1jDYBCDrAOAwmT8B4U2/aVdnKfKixp2fvNpkq9h8TbDQXWze8JvXocsZlY+Nv6/OU87oGcmZPSMpKXPwz29+bfg9K4actJC+hRXBwlp+Nsqt2Z/JFXNWcPBYAZ3DA/h41tjWGygE6DHVbPcvg6LjJz3tZbPy5wv7cnv5YJuHPtzMij1Hm3OFLVZaTiEfrTO/U+4oHwwDmL+3APGjaj7RYoGQDqav6chb4fx/ww2fwf3b4eGDcNsiuPwVOONB6HOxKYsvK4afHnfr1+MOVquFRy/syyPn9wHg9eX7ufvdDRSVlnl4ZU1TWFLG/NXmzafp47tUBog7DnNfAEuIHXEJOQ5/QkuO4HAG89xh/zLzdy4sAcK7u+8+pwH9tIuIiIhIq/bFpsPkFZfROTyAMV1rCQRWnYTsit6AziEn+5c2rjefM6tw0DQIimz6euri7QdXLzAlyePuhY7DT3nKIxf0wWqBb7elsmZ/ZsPuVzHkpIVMRHaWIdeRWfj1lhSue3U12QUlDEkI46M7xtIpvAEZlS1RRA9o19m8MK4jqP2Hc3tz4cAOlJQ5uP2tdRp6Asxdvo/iMjvDO7VjeOfyYUklBZCyyew3phzVNxhih5iWA5MehWlvwQ2fg8UKu76Bg2td9wU0o1vP6MqzVw/G22bhq80p3PjaL+QUlnh6WY32yYZDZOYV07GdP1P6xcDBNeYJ9St0q3G9O/KDYwQAWb8scN+NqpYgu6pP8GlKwUIRERERadUWrDHBoGtqG2wCVSYhN7EE2anDIPBvB0U5ZshBQ2Qmwo4vzf4JE4ndxjcIrngVJv+9Xof3jA5m2ogEAB7/agd2ewP6kVUEC7c1dJXuUREsrDmz8NWlidz5znqKS+1M7hvNOzNGEx7k24wLdBOLpTK7cPd3tR5mtVp46spBjOzcntyiUm56/RdSswubaZEtT3ZBCfNXmcyyalmFhzeY/p9BMSYryRUiusOga81+K8wudLpkcBzzbh5JkK8XqxIzuerFla3yZ8jhcFQMNrlpbGdsVktlZmH8CA+u7PQX4OPF/g7nA+C363Moc1PAec+PZttNJcinomChiIiIiLRa2w5nsyk5C2+bhSuG1dFv7Vh5ZmFTJyE7WW2Vpb0NLUVe/RLgMJkNUX1csx43uH9yTwJ9bGxKzqpo9l8v0X3NNjcF8jLcs7iGqKVnod3u4LEvtvP4VztwOOCGMZ148bfD8PexeWCRbtJzitnu+r7Oyd1+3jZevmEYXSMDScku5OZ5a8htxdlhTfH2qgMcLyqlV3QwZ/WKqnyiogR5pGszks58CKzeZrr6/mWuu24zG9c9gvduH01ksC+/puZyxZwV7ElrXVmqS3YfZXfacQJ9bFw1Ih6Op1f+2xF36oxsaZq4IVNJd4TgX5rd+BYfdclMhMy9ZlJ81dYcUiMFC0VERESk1Xr3F5M1NqVvDBF1ZYNVZBY2cRJyVc6+hXt/rv85Bcdgffm0xzF3um4tbhAZ7FuRWfWvb3dSWFLPXmS+wZVB2SMtoG9hVnlmYVhlZmFhSRl3LVjPa8tNIODh83rz94v7mUyi00mn8eAdCMdTK0toaxEWYIaeRAT5siMlh1nz11NSZm+mhbYMhSVlvL58PwC3n9m1+rCOigyzWvoVNla7TjD0BrP/0//VGdRt6frFhvLxHWPpGhnIoawCrpizkrUNbWPgQXPLswqvGhFPiJ93ZQlyZG/wD/PcwtqISf3i+No+GoCC9e+6/gbOrML40eAX4vrrn2YULBQRERGRVim/uJRPKwabnKIssGrPQldx9i08+EuNAyRqtO4NKMmDqH6VwcYWbPr4rsSE+HEoq6AiiFIvFUNOPNy3sKQA8suHdpRnFh7LK+a3r67m6y2p+NisPHv1YG4/s1vtJeytmbdf5c/p7u9PeXh8+wBeu2k4/t42lu4+yp8+3oKjFQevGurDdQc5eryIuDB/LhoUW/mEw+G+YCHAGQ+YYUdJK2DvT66/fjOKbx/AhzPHMiQhjOyCEq57dTXfbUv19LJOafeRXJbsSsdigZvHlv87cdA53EQlyM0hMtiXHREmG9pr19dQnO/aGzj/bmkKcr0oWCgiIiIirdJXm1PILSolvr0/Y7vVMeG4rBSyTA8yl5Uhgwk8tuts+pgdWH7q48tKykuQMVmFrSA45e9j48GpvQB44ec9ZBwvqt+J0QPM1tN9C7NNMBmfIPALIzkznyteXMHaA8cI9vPijVtGcsngOM+u0d0qSpFr71tY1cCOYTx/7RCsFvhg3UGe+3GPGxfXcpSW2Xl5iclAnjGhC962Ki+VMxNN0NnmCx0Guv7mIbEwYrrZ/+nxVp1dCNA+0Id3Zozm7N5RFJXauePtdXy07qCnl1Wn18rfDJnSN5qE8ADzYHJ5ZmFjBtpIoyQMnEiyPRLvsnzY9a3rLlxaDImLzb6ChfWiYKGIiIiItEoLfjEBwKtHJFQvFzxRzkGwl5gX+iEuDgw5s7bq019p26eQexgCo2DAb1y7Dje6bEgc/eNCyC0qZfYPu+t3UnQ/s/V0GXKV4SZbDuVw2QsrSEzPIzbUj4/uGMuYuoLMp4se5cHCQ+tMD7Z6OLtPNP+41GSHPvPDLj5s4YEeV/hmaypJmfm0C/Bm2ogThuE4swpjB4OXm4bfjL8PvAPMwKSd37jnHs3I38fGS9cP4+oR8dgd8NBHm/m+hWYYZuYV8/F68zM+fXx5q4qyEvN3BjQJuRlN6RfDF/YxAJRu/sB1F05eZbL6A6Mq38ySOilYKCIiIiKtisPhYFViBuuTsvCyWrhyeB2DTaCyBLldJ7C6+L+/9Q0WOhyw8r9mf+Rt7gs4uIHVauGR883Qknd+SWJPWj1Krp1lyOk73TfVsj7Kg4UZXlFMe3klR48X0adDCJ/cOY6e0cGeW1dzComFmIGAA/YsrPdp143qVNGz8o8fbWbp7voFGlsjh8PBnEV7AbhpbBcCfLyqH1B1uIm7BEXBqNvN/s//B/bW3y/Sy2blicsHcOWwjpTZHdy1YAMr97aAoUcneGf1AYpK7QyIC2VE53bmwSNbobQA/EIhoqdnF9iGdIsMYk3wJAAsexZCQZZrLrznB7Ptfrbr/x9wmtKfkoiIiIi0eKVldlYlZvD4l9s566lFXP3yKgDO6RNNVLBf3Sc7p1m6criJU5czAQukbYfcOrJmDiw3Aya8/GH4La5fh5uN6RbOOX2iKbM7+Oc3O059Qlgn8A2BsmI4uuukp4tL7Ww9lM37a5L5y2dbeejDTbyzOoldR3Kx211Yglk+Cfm7g17kF5cxvnsE798+muiQU/zMnG56TjXbepYiOz04pRcXD4ql1O7gjrfXsyMlxw2L87ylu4+yPSUHf28bN4zpdPIB7uxXWNXYe8zfmyNbYfun7r1XM7FYLDxx+QCm9I2muNTOrW+uZcvBbE8vq0JxqZ03Vx4AYPr4LpW9S50lyHHDFVxqRhaLhe79R7HT3hGbvQR2fOGaC+9x9is8xzXXawO8Tn2IiIiIiEjzO15UypJd6SzcfoSffk0ju6AyQ83HZmV8jwj+enHfU1/IOQnZlf0KnQLaQ4dBkLLR9EMaNK3m41b+z2wHXwOBrbP09eHze/PzzjR+2JHGir1HGdstovaDLRZTipy0ksKDm9hW2IGth3LYdjibrYdy2J2WS0lZ9aDg+2tNYC/Ez4uhndoxvFM7hnVqz+D4MPx9bA1er8PhYNuObfQHDtrDuXxoHP+8fCA+Xm3whX+PqbDk36bBf1kJ2LzrdZrVauHfVw7kSE4hq/dlcvPra/jkzrF0CPV384KblzOr8JqRCbQL9Kn+ZGG2eTMA3F+OGtDe9DNd9AT8/P+gz8Vga/0v2b1sVp67Zgg3vf4LqxIzufH1X/hg5hi6RQZ5eml8ufkwablFRAX7cv6ADpVPOIebqF9hs5vcN4bPVozlIev72Ld8gHXo9U27YE5KeUsMS6sYLNZStP7fPCIiIiJy2kjJLuCH7UdYuCONVXszKC6rLMVrF+DNWb2jmNI3mvE9Ignyred/Zd0xCbmqbmeVBwt/rjlYeHRPZQ+y0bPcs4Zm0C0yiOtGJfDmygP831c7+OKu8Sf1ijyWV8y2wyYo2CcnmjOANz75iidKQ0+6Xqi/N/1iQ+gXG4Kvl411B46xMTmLnMJSFu1MZ9FOU/bqZbXQNzaEYZ3aMbxTe4Z3bnfKzMCSMjsPf7yFy1MSwQb9+/bnwSsHnZ4Tj+sjbigEhEN+BiStgi4T6n2qr5eNl68fzm9eXMHutOPc/Poa3p85hhC/GgKOBVngGwzWhgd3PWVjchYrEzPwslqYMaGG3xEH1wIOM8woONr9Cxp9B6x+ETJ2w5YPzBsMpwE/bxuv3DCca19ZzZZD2dww1wQMY8M8F3h2OBzMXWb+fbhxbOfqbyQkK1joKUMTwnjM5wwecryPZf9Sk7UfHNP4CzqnIMcOabVv1nmCgoUiIiIi4jEOh4Nth3P4YccRfthxhK2Hqpc5dokIZHLfaM7pE83QhDC8bI3ICst0YxkymL6Fy54xfQsdjpOnHK+eAzig57kQ0cM9a2gmvzu7B5+sP8S2wznMW7GfhPYBbDucw9bD2Ww/nMOhrIKKY6+xhXOGN/SxHCA6xJd+saH0jw2hb2wo/WJD6NjO/6TgXUmZnV9Tcll7IJO1B46xbv8xUnMK2Xwwm80Hs3m9fGJpXJg/wztXZh/2ignGVh64zC0sYdb89SzdfZS7fU1/tPPHj2gV06fdxmozg042LTATRhsQLAQIDfDm9ZtHcNkLK/g1NZdZb6/ntZtGVA+ubHoPPr8LQjvCRc9ClzNc/EW4x4vlWYWXDI6rOXDVXCXITn6hMO538MPfTIbhgN/UOxO0pQv282bezSO48sWVJB7N4/q5q/lg5ljan5jN2UxW78tk2+Ec/LytXDsyofKJ42mQdQCwmDJkaVZeNiu9+gxk/dbuDLXugW2fmCB6Y+390WxVgtwgChaKiIiISLMqKi1jVWImP2w3AcKU7MKK5ywWGJbQjnPKA4Tdo5pYpuZwwLH9Zt8dZcgA8aPByw9yU0x/vshelc/lZ8KG+WZ/zJ3uuX8zCg/y5c5J3fnnN7/y2JfbazymU3gA/WNDGRY0ATbMZXxwKqsfqt+LNG+blQEdQxnQMZSbx5nv16GsAtbuz2TdgWOs3X+MX1NNUPLQxgI+23gYgCBfL4YkhDGsUzu+33aE7Sk5BHhbiLdlgh0TwGrrnMHC3d/D1P9r8Okd2wXw+k0juOqllSzbc5Q/fryZ/zizNVc8D98/Yg7MTIQ3LoIh18OUf4B/Oxd/Ia6zN/043203vUZnnlnLmwnNMdzkRCNvM60Lsg7Ahrdh+M3Nd283Cw/y5a0Zo/jNnBXsTc/j5td/Yf6to+ufKe5Cr5VnFV4+tGP18nNngDiqD/iFNPu6BCb3jebzTWMZat2DY8uHWBobLLSXVWYWdj/bdQtsAxQsFBEREZFmsTf9OE9/v4vFu9I5XlRa8bi/t40JPSI4p280k3pHERHkwknBx9OgJA8sVghLOPXxjeHtBwljTBny3p+rBwvXvmYmasYMgM4Ny+ZqqW4a25lPNxxid9pxekQF0Tc2pCJrsE9sSGV5anEv2GDBmp9uvg9BUY26X1yYP3GD47hkcBxgelluTMpi7QETQNyQlMXxolKW7j7K0t1HAYgI8uHNqzphfafEfO+DY13ytbdq3SaBxWYC2pn7GlWW3z8ulP9dN5QZb6zl4/WH6Bjmz/28DSueMweMmml6Iq6dCxveMgNVzv839L2kRWZ2vrw4EYfDDErqUdN0bHtZeRkyzZdZCOATCBN+D9/+0fSaHHSN+T1zmogL8+et6SO58sWVbDqYze1vreW1m0bg69V85evfbUtl4Y4jANwy7oS/C85+hR1HNNt6pLozekbwmGUsf3a8he3QWvMmRGOqAw5vgIJj4BuqLNEGUrBQRERERJrFk9/8yvfbzYuzyGBfzukTxeS+0YztFoGft5teJDqHm4R2BC83lrp1nWiChYmLYPRM81hpEfzystkfc3eLDJY0hp+3ja/vmUCp3VH3sBCfQAjvBhl7IHWLy7I6gny9GN8jgvE9zICVMruDnam5rCsPHh4vKuUvF/YjoaA88zE49rQYEtFk/mEmqH1gmckuHHV7oy5zVq8oHr+0P3/+eAMJSx8A2xLzxDl/g3H3mp/zAVfCF/eYwOQHN0Kv8+H8pyA0zlVfTZOlZhfy8QYzVOeOid1qPij9VyjOBZ8giKrHMCVXGnYzLH8Ocg7BunmVv1dOE92jgpl380iufWUVy/dkcO+7G3n+2qEV7QTc5VheMX//Yhuflmcln9c/5uQMduckZPUr9JgAHy96d+/Oir39mGDbCls/gjMebPiF9vxgtt0m6t+BBmqDo8BEREREpLmV2R2sTDT94164biirHz6bJy4fyKTe0e4LFAIcK+9X6K4SZKdu5RMW9y8zmVUAWz+G40cguAP0u8y9929mVqulflOFo/uZ7ZFtbluLrXwAyvVjOjP76iG8euMIEsIDIDvZHKAS5Eo9p5rtrm+bdJlrBkfwXcxL/Ma2hFKHlV9H/RPG31cZEO80Bm5fCmc8BFZv2Pk1/G8UrHkV7Pa6L95M5i5LpKTMwcgu7RnWqZZSaWcJctyw5h/a4u0HZ5YHR5b+B4rzmvf+zWBQfBgv3zAcH5uVb7am8sgnW3A4HKc+sZG+3ZrK5GeW8OnGw1gtcPuZXXlm2uDqB5WVmGw0cP/0a6nT5L7RfG4faz7Z8qFpK9JQe9SvsLEULBQRERERt9t2OJvcwlKCfb2Y0jf6pCm6buPu4SZO0QPMtNniXDi0zryoWfk/89zI29yb1diSRQ8w2yNbm//e2SZrTMHCKpzBwv3LoOh4466RnwlvXkK3rOUUW3y5reR+rljZhW2Hs6sf5+0Hkx6B25eYcs7iXPjq9/D6eZC+s2lfRxNl55fwzuokAO44s5asQmj+4SYnGvxbCOsEeWnwyyueWYObjesewXPXDMZqgXfXJPPkt67/2cjMK+buBRuY+fY6jh4vontUEB/dMZaHz+tz8ptVqVtM6wi/MAjv7vK1SP2d3SeK78pGUOTwMlm+DX3TKT8TDpW3EeimfoUNpWChiIiIiLjdyr0mq3BU1/aNm2jcWM4y5Eb0Z2sQqxW6nGn29/4M+xbDkS3gHQDDbnLvvVuymP5mm+qBYGFWeWZhWHzz37uliuhpgk9lxeZntKGyD8Jr55qebn5hWG74jILOk8krLuPm19dUm4ZdIbov3PIdnPdvU86bvApeHA+LnoTS4qZ/TY3w1qr95BWX0TsmmIm9Ims/sGK4iYeChV4+MPGPZn/5bCjMqfPw1urc/h144nLzxsKLi/fy0uK9Lrv2N1tSmPLMYr7YZLIJZ03sxpd3j2dIQi3ZpAfLS5A7jjC/18VjooL96Bofx8/2IeaBLR807AKJi8Bhh8g+LaoFQmuhn34RERERcbsV5cHC0V3Dm/fGzVWGDKZvIZgXKM6swsHXQUB799+7pYouDxYe3Wl6ODYnZRaezGKpUor8XcPOTd8Jc6eY72VwLNzyLd5dxvDi9cPoGR1EWm4RN7/+C9kFJSefa7XBqNtg1iozlbmsGBb9P3jpjMrsvWZSWFLG68v3A6ZXoaW2XqLH0yvfbOjowcEIA66C8B5mSMOqOZ5bh5tNG5HAH8/rDcAT3/zK+2uSm3S9jONF3PnOeu6Yv56jx4vpGR3EJ7PG8dC5vetufVGRTaoS5JZgct9oPi8bYz7Z+nHD2hjsdZYgK6uwMRQslOaRtNq8C7l/uadXIiIiIs2spMzOmv2ZAIztFtG8N6/ILHRzGTJU9i08+IsZIIEFRt/h/vu2ZKEdwS8U7KVm2EVzquhZqMzCapzBwt3f178HWPIv8NpUM2wjoidM/x6i+gAQ6u/NvJtHEh3iy64jx7ll3hqy82sIGILJ8rz2fbhiLgREQPoOE4D8+iEoynXBF3dqH6xNJiOvmI7t/LlgQIfaD3ROxI3sY4bDeIrNC8562OyvfN6UVp6mZp7ZjdvPNL+r//jxZr7dmtKo63y1OYXJzyzhq80p2KwW7jqrO1/cPZ5B8WGnPlmTkFuUKX2j+dE+lOMOP8hOqvz+nIrDoX6FTdSoYOELL7xAly5d8PPzY9iwYSxdurRe5y1fvhwvLy8GDx7cmNtKa5V3FN6/HpJWwrd/bFxjUhEREWm1Nh/MIr+4jHYB3vSOCW6+GxdkmWwcgHad3X+/sAQTlHSUZz70vsBMA27LLJbK7MLmLkWuyCxUsLCaTuNNeXxuCqRuPvXxu76HNy42f5fihsPN355U2h0b5s9rN40g2M+LdQeOcdVLK0nNLqz5ehYLDPgN3LUGBl0LOOCXl+B/oxue7dhApWV2Xlpi3kC47YyudbdEqChBbgEZZn0vg6h+UJRjAoansT+e25tpw+OxO+CeBRtZsedovc9Nzy3ijrfXcec768nMK6Z3TDCfzhrHA1N74etVjwE1uamQlQRYzFAb8bjuUUHEhIfxnb08u3fLh/U7MW27+R3n5W+mwEuDNThY+N5773HvvffyyCOPsGHDBiZMmMB5551HUlJSnedlZ2dzww03cPbZSgFtU+x2+PQOMwkQzH9IDii7UEREpC1ZWaUEudkGm0BlCXJgFPgGNc89u55VuT/mzua5Z0vnDBY255CT4jwoKM/AUhlydd5+lSXzu76v+9iNC2DB1WbgQ/fJcOPnEFhzK4F+saG8f/sYooJ92XkklyvmrGBPWh1DVALaw2Vz4PpPTB/FnIPwzlXw4S1wPK1xX9spfLUlhYPHCmgf6MOVw04RRPb0cJOqrFYzLAZg1YumRPo0ZbFY+L/L+nNuvxiKy+zc+uZaNiVn1XmOw+Hg802HmfLMYr7ZmoqX1cI9Z/fg87vGM6BjaP1v7vyeR/UFv5DGfxHiMhaLhcl9ovm8bJx5YNsnUFZ66hOdWYVdJpjfedJgDQ4WPv3000yfPp0ZM2bQp08fZs+eTXx8PHPm1N0/4fbbb+faa69lzBhFdduU1XNMiYPNt/I/zytf8OyaREREpFk5+xWO6dbM/QqbswTZqdf5ZttxhLIZnCqGnGxpvns6swp9Q/WivyY9ppjt7joy+ZY/B5/OBEcZDJwG1ywAn8A6L9unQwgf3TGWrhGBHMoq4MoXV7Ah6Vjda+k2yfQyHHsPWKyw9SN4fgRsmO/SiiSHw8GLi83vhJvHdsbfp45Ms9JiOLTe7LeEYCGY3y2xQ6Akzww7cafsg7D5/ebvM1rOy2Zl9tWDGdstnLziMm56/ZdaA89puYXMfHsd9yzYwLH8Evp0COHTO8dx/+Se+Hg1MNzhLHGNVwlySzK5bzTL7f3IJBjyj8K+Rac+ac8PZtvAEuTiUjvzVx/Ablc1ZIP+9hQXF7Nu3TqmTJlS7fEpU6awYsWKWs97/fXX2bt3L3/961/rdZ+ioiJycnKqfUgrdHgDLCz/nk/9PzjvX2Z/59eQ4boJVyIiItJyFZaUsfaACRaMbfZgYXlmobsnIVfV4xy44TO45l1TbinVMwubqx1NRb9CZRXWyBksPLjWtAyqym6H7x6BhX82n4+5Cy59EWze9bp0fPsAPpg5hkEdQzmWX8K1r6zm552nyBT0CYAp/4Bbf4KYAVCYBZ/Ngk9nNezrqsPiXensSMkh0MfGDWM6131w6mYoKwL/9i2nlYDFApMeNftrXoWcw66/h90Ov7wC/xsFH98KX97n+nvUk5+3jZdvGM7A8p+j6+eurjZt2+Fw8NnGQ0x5ZgnfbTuCl9XCvef04LM7x9E/rgHZhFUlOycht4DSc6kwrFM7ggL8+bJ0tHngVKXIRcdNCzSAbvWvbHU4HPzls6088slWHvhwUyNXe/poULDw6NGjlJWVER0dXe3x6OhoUlNTazxn9+7d/PGPf2T+/Pl4eXnV6z5PPPEEoaGhFR/x8eoz0uoU5ZoSAnsJ9L4QRsyAyJ6mfAEHrH7J0ysUERGRZrAhKYviUjuRwb50i2ymUmCn5pyEXFXXiRDYzINcWrKoPiZjLD+jsjWNuzkzC8P0OqJGoXEmKIcDdi+sfLysxLQQcvbFm/wP86a/tWEZWuFBvrxz62jO6BlJQUkZt76xlo/XHzz1ibFD4Naf4Zy/AxbY9A4cO9Cge9dmziKTrHDNyARCA04R+KxagtySgv7dzjYZy6WFsPQ/rr12+i54/Tz4+gEoLs/i2zj/1KXqbhTk68W8m0fSLTKQlOxCrp+7mozjRaTlFHLbW+v43bsbycovoW+HED6/azz3ntOIbEKn0mKT7AIto0+lVPCyWZnUO6pyKvKOL6GkoPYT9i8zE9fDOjUo2D9vxX7eXZOM1QIXD4pt4qpbv0b9TTpxvLzD4ahx5HxZWRnXXnstf//73+nZs2e9r//www+TnZ1d8ZGc3LSx6eIBXz9oSn9C4uDi/1b+Izum/N3BDW+bpuMiIiJyWlu512QtjekaXuP/F92qIrOwGcuQ5WTe/hDe3ew315CTLGUWnlIP51Tk8lLk4jxYcA1sfhcsNpNNOO6eRl8+0NeLV28YzmVD4ii1O7j//U28vKQe1UU2bxh/L3Qebz7f9kmj1+C0PukYq/dl4m2zMH1CPd48qBhu0sLKUatmF657wzWB1LISWPIUvDgOkleBTxCc/xSMKp/k/sXvPPq6rX2gD29NH0VsqB+J6Xlc/fIqJj+zhIXbj+Bts/D7yT357K5x9I1tYruB1C3l2aTtKn9fSYsxpW806xw9SbVEQnFu3cOQ9laZglzP/3cs2ZXOP77cDsCfzu/DxF5RTV1yq9egYGFERAQ2m+2kLMK0tLSTsg0BcnNzWbt2LXfddRdeXl54eXnx2GOPsWnTJry8vPjpp59qvI+vry8hISHVPqQV2fQebFpg3kG+4lXTvNip61mmYWxJHqx/03NrFBERkWaxMtH0K2z2EmTwTBmy1KyiFLmZ+hZWTEJWsLBWPcuDhXt+MgNF3rwE9iw000OveRcGX9PkW/h4WfnPlYOYMd78Hfx/X//K/321vX79wPpdZrbbPm7SGopL7Tz3424ALh0cR4dQ/7pPcDiqBAtbSL/CqjqPN9nL9hJY8q+mXevwBnj5LPjpHyYTq/s5pn/kyFvh7L9A+26Qe9iUpXtQbJg/b04fRftAH3anHSe7oIT+cSF8cfd47j67B951TbWuL2e/wo4jW1Y2qQAwoUck3l5efFpSXoq8tY5S5Ab2K0xMP85d76zH7oDfDOvI9PH6PwM0MFjo4+PDsGHDWLhwYbXHFy5cyNixY086PiQkhC1btrBx48aKj5kzZ9KrVy82btzIqFEt8JevNE3GXvjqfrN/5h+g0wk/FxYLjC5/l+qXl+s3yUhERERapfziUjaWT7Fs9uEmJQXmRS4os7AlqBhy0kyZhRXBQpUh1ypuGASEQ1E2zBkLB9eAX5iZeNxzyilPry+r1cKjF/blT+f3BuCVpfv4/QebKCmz131i30tMhmPKpkb1Oy8ps/PuL0mc9dQiFu1Mx2KB28+sx++C7IOQm2LuHTu0wfdtFmeVZxduXABH9zT8/JICWPgXeOVsE8D3bweXvQzXfVhZuu8TAJf8D7DAxrerl6t7QPeoIN68ZSRjuobz0Lm9+GTWOHrHuDCpKFnDTVqyQF8vxnUL57Oy8vjCru+hMPvkAzMTzYfVy0xCPoXsghJmvLGWnMJShnVqx/9d1r/5qyBaqAaH4O+//35effVVXnvtNXbs2MF9991HUlISM2fOBEwJ8Q033GAubrXSv3//ah9RUVH4+fnRv39/AgPrnqYlrUxpMXw03fS46DQOzniw5uMGXAUBEabx9I7Pm3eNIiIi0mzW7j9GSZmDuDB/EtoHNO/Nj+03W99Q80JYPKvqkJPmkJ1ktgoW1s5qK+8nDuSlm/ZBt3zntn5tt53Rjf9cOQib1cInGw4x44215BfXkTgQGAFdzjD7DcguLLM7+GjdQc55ejF//HgLh7IKiAr25ZmrBtM9KvjUF3BmFXYYaAJmLVH8COh5rplUvfifDTt3/zITHF7+rDm//xVw5xoYNO3kjLpOYyoTPT6/x+NtpPrHhbLgttHMmtjdNdmEVR3UcJOWbnLfGHY4Eki2xZuS8R1fnnzQnvIS5IQx4Fv33/fSMjt3L9hA4tE8YkP9ePG3w/D1qmNKehvT4L9h06ZNY/bs2Tz22GMMHjyYJUuW8PXXX9OpUycAUlJSSEpKcvlCpRX48e8mld0vDC5/2fwHpCbefjBiutlf9UKzLU9ERESa14q9pgR5tEf7FXZWSVlL4AwWHt0NJYXuvZe9rHJSrMqQ69b/crON6AXTv4eo3m693RXDOvLqjcPx97axeFc617yymsy84lOvb+up+xba7Q4+33SYyc8s5vcfbOJARj4RQT48ekEfljx0FpcOiavfIqsON2nJzvqT2W75EI5sP/Xxhdnwxb0w7wKTeRXcAa5eAL95DYIiaz9v0p9NdnbuYfjes+XIbpOTYhJZLFaTcSst0jl9ogAL7xU6pyJ/cPJBzmBh91NPQX7im19Zsisd//LJ25HBvq5b7GmgUeH4WbNmsX//foqKili3bh1nnHFGxXPz5s1j0aJFtZ77t7/9jY0bNzbmttKS7f6hcmrapS+c+j9mI2aAzce8g+McUS8iIiKnFc/2K0w0W5UgtwwhsSbD01EG6b+6917Hj4C91JShBce4916tXc+pcNtiuG1RswVWz+oVxfxbRxEW4M2m5Cx+8+IKDh7Lr/ng3hea72PaNkjfWeMhdruDb7akcO6zS7hnwQYS0/MIC/DmD+f2ZslDZzFjQlf8vBuQLVTRr7CFZ5h1GGRKtXHAov9X97E7v4H/jYZ1r5vPh90Md66G3uef+j4+AXDJC4DFDKn0cDmyWzj7FUb1A98gz65FahUV4seg+DC+sJdPRd632PRbdSotgn1LzH63uoOF769JZu4y86bif64aRP+4UHcsuVVzce6utEm5R+BTU4bOiFuh9wWnPicoCgZcafZX/c99axMRERGPyCksYcvBLMAD/QoBjpVnFrZTo/IWwWJpvlJkZ7/CkNjaK12kUuzgZi+3HZrQjg9njiUuzJ/E9DyumLOCX1NzTj4woD10m2T2T5iK7HA4+GH7ES787zLumL+eXUeOE+znxf2Te7L0obO4Y2I3Any8Graw4jwzFRdafmYhwMSHAQvs+MJUeJ3oeDp8eAssuNpkBrbvCjd+CRfNBr8GBEdOLEeuqVdca6Z+ha3GlL7RHHDEkOjTGxz26r8XklaZQapB0RAzoNZrrN2fySOfmr/nvzu7B+cP6ODuZbdKChZK09jt8Mltps9JVD+Y8nj9z3X+g7P9c8hKds/6REREqkjKyOfG137hyW/dnNkk/JKYid0BncMDiA07xfRRd9Ak5JbH+eLN3UNOstSvsDXoHhXER3eMpVd0MEdyirjyxZX8si/z5AP7OUuRPwaHA4fDweJd6Vz6wgpmvLmW7Sk5BPl6cc+k7iz7wyTuObsHwX7ejVvUofUm+zUkrnWUsEf1qUzA+LlKdqHDAZvfh/+NhK0fmfLacb+DO1bUa+hDjaqWI3/3p6avvSVRv8JWY3LfaADeKSj/Xm2pMhV5b3kJcreza20/ciirgJlvr6OkzMF5/WP43dk93LncVk3BQmmaFc9B4iLw8ocrXzf9COsrZoBpWuwog19ectsSRUREANYdyOTSF5azeFc6cxbtZenudE8v6bTmLEEe0y3CMwtQGXLLE93PbJsrs7A1BHvauJhQP96/fQwjOrcjt7CU6+eu5vttqdUP6n2+aV90dCcb167gyhdXcuNrv7ApOQt/bxt3TOzG0ofO4v4pvQj1b2SQ0Km1lCBXNfGPZnLz7u8habVJwph/JXx8KxRkmozeGT/C5MfAuwlv3FSdjrzhbdOG6nRQWgyHN5r91vR9b6N6RAWR0D6Az0pG4cBqSsidA81O0a8wv7iUGW+s5ejxYvp2COE/Vw3CalVP49ooWCiNd3At/PQPs3/ekxDZq+HXGH2n2a57E4qOu25tIiIiVXyx6XBFI/0AH1OW+NgX2ykts3t4Zacv53ATj5Qgl5WaZvWgMuSWpGoZssPhvvtUBAuVWdgahAZ489b0UZzTJ5qiUjsz317Hu79UGZjpF8qx2DMBWPrZy6w9cAxfLyszxndh6R/O4g/n9qZdoI9rFtNahptUFd4NBl9r9j+bBS+Mhj0LTYB10qOmH2XcUNfcq9NYGFXefuqL06QcOXWzmawbEK43l1oBi8XC5L7RpNOO3YFDzINbPzJDao5sBSyVrQuqsNsd/P79TexIySEiyIdXbhze8DYFbYyChdI4hdmm/4W9FPpdBkNvaNx1ekyB8O5QlA0b57t2jSIi0uY5HA7+9/Me7l6wgeJSO5P7RvPT7yfSLsCb3WnHmb866dQXkQY7llfMjhTTf2xMVw8EC7OTzf9RvPzMxE9pGSJ7mwyogmOV04rdwRkoVmZhq+HnbePF3w7l6hHx2B3wx4+38N8fd7Mh6RjXz13NX/eapISLbKu4cXQCSx46i0cv7EtEkAunlzoclYMuWls56pkPgdUbMvZA8XET7Jy5HM54EGxNzLY80dl/MW/C5ByC706D6cjObNKOI2otXZWWxVmKvCC/vMfklg9h709mP26o6XV6gmd/3M03W1Pxtll48bfDiPNEe5RWRsFCaTiHA768D7IOQGgCXDi78b9YrdbKd6dWzQF7mcuWKSIibVtxqZ0HP9zMv78zEzSnj+/Ci78dRkyoH7+fYl54Pr1wF8fyij25zNPSqvIS5B5RQUQGu/DFfH05S5DbdTb/15CWwdsPInqafXeWIiuzsFXysll54vIB3D2pOwD/WbiLy15YwdLdR1nEMIotvnS2pPL3kWVEhzSg9VF9ZewxgWwvvzqHI7RIYQkmizCkI5z3b7j5W4js6Z57+QTApc7pyG+1/nJkZzZpRw03aS2Gd2pHWIA3HxUMxW71gbTtJpYA0P2ck47/anMKz/64G4D/d9kAhnc+OZgoJ9P/nqThNs4vb5Rrg9/MBf+wpl1v8LXgF2amFu76zhUrFBGRNi47v4QbX/uFD9cdxGqBf1zanz9f2BdbeW+aa0Ym0DsmmOyCEmb/sMvDqz39OPsVjvVECTJoEnJLFlNeiuycOOsOyixstSwWC7+f0ou/X9wPiwVsVgtXDuvIVw+ci0+fc81B2z52z82dGWaxQ8HLRWXNzWn8vXD/Nhh1m/vfJPFUOfLWj+C5oaYn49rXITf11OecinO4ifoVthpeNiuTekWRQxC7Q0abB4+U/5tyQrBw66Fsfv/BRgBmjO/ClcP1JlJ9KVgoDZO+C75+0OxPesQ1v1R9AmHYTWZ/1QunPNxud7D7SC4l6jMlIiI1SMrI5/I5y1mZmEGgj425N43g+tGdqh1js1r4y0V9AXh7dRI7U3M9sdTTlkf7FUKVScjqP9XiVAw52eae6xfmVAYuFCxstW4c25lvf3cGix6YyL+vHER8+4DKqcjbPnFPz8vWONzEk87+c/OVIxfnw+f3mDZYmXvNMJcv74X/9IJXJsGSp+DI9ob/XGQfMuu3WE2QWFqNilLkgip/X/1Cq30f03ILufXNtRSW2DmzZyQPn9+nuZfZqilYKPVXUmh+QZfkQ5czYdx9rrv2yNvA6gX7l0LK5hoPKSwp453VSUx+ZjGTn1nCwx+78R1pERFplZwTj/em59Eh1I8P7xjLWb2iajx2bLcIzu0XQ5ndwT++3I7DnQMX2pC0nEL2pB3HYoFRXTwdLFRmYYsTXV7e6a4yZGcJsn878A1yzz2kWfSKCTZBQqceU8A7ELKS4NA619+wNQ438SSfwOrlyHvcVI6c9iu8ejasf8Pca9zvYNKfIW6Yef7QOjN0c84YeG4wfPsw7FtqBl2dirNHZXQ//b5oZc7oGYmPzcq72f2weweaB7ueBTYztKSotIyZb60jJbuQrpGBPHfNkIrqEqkfBQul/n74q0nvDQiHy15ybXp7aBz0vdTsn5BdmJ5bxNMLdzH2nz/xp0+2sDc9D4CP1h8kMV0TlEVExKg68bh/XAif3jmOPh1C6jznT+f3wcfLyrI9R/lhR1ozrfT05ixB7tshxHUTShtKZcgtl7MMOWMPlBS4/voV/QqVVXja8QmAXueZ/a0uLkUuOAbpv5p9ZRbWX9Vy5M9dXI7scMCGt+GVs0xPusAouP4TmPwYnPEA3PoT/H6n6Z/fYyrYfOHYfvNa8o0L4d/d4OPbTCZqUS3VA8nlJcitbaCNEOjrxdju4RTiy7bI8t8L/U32scPh4E8fb2V9UhYhfl7MvXEEof4uHvTTBihYKPWz8xtY/aLZv/RFCHHDZMExs8x2y4eQm8quI7n84cPNjHvyJ577cTeZecV0bOfPXy7syxk9I3E4YM6iva5fh4iItCo1TTx+//Yx9WqAnxAewIzxJqD0+FfbKSrVoK2mWuksQfbEFGQwLzCVWdhyBUVDQAQ47CYA4GoV/QrVl+q01L9KKbLdhS2JDq412/bdIDDCdddtC6qWI3//qGuuWZRrAn2f3VlZ1TZzGXQ7q/pxwTEw/Ga47n14KBGmvQ2DrgX/9lCYBZvfgw9ugn91hbevgDWvVp/EflDZpK2ZsxT5sZLr4Y4V0PcSAF5duo+P1h/EZrXwv+uG0iUi0JPLbLUULJRTyzkMn5YH8kbfCT2nuOc+ccNwxI8GewmfvfIYU55ZwntrkykutTMkIYwXrhvKogcmcsv4Ltx7Tg8APtlwiOTMfPesR0REWrwTJx7PKJ94HODjVe9rzDqrO1HBvhzIyOf15fvdtNK2o2K4SXcPBQtzU6G0wAxiC0vwzBqkdhZLZd/CVDeUIitYeHrrdjb4hkDu4coeg65Q0a9QQaMGq1qOvP7Nppcjp2yGl86ELe+bXoKTHjUZhcHRdZ/nGwR9LoLL5sCDe+Dmb2Ds3SYAXFZs1vXV7+HpPub6i56ElE3m3HhNQm6NzuljfibWHswjLaAbAD/vTOOJb3YA8OgFfZjQI9Jj62vtFCyUutnLzLs6BZkQMxDO+atbblNUWsYHa5N5PGMiAOOzP8ffUsx5/WP46I4xfDJrHOcP6ICXzfzIDk1ox/juEZTaHby0RNmFrVVhSRl7VUouIo1U08TjR6tMPK6vIF8v/nBubwCe/2kPabmF7lhum3Aoq4ADGfnYrBZGdG7vmUU4S5BDO4JNZUctUoyzb6EbhpyoDPn05u0Hvc43+66ciqzhJk3TaSyMut3sN7Yc2eGAX14x/Qkz90JIHNz0NZzxIFhtDbuW1WbWNOVxuHsd3LkGzvlbeTDYAikbYdH/M0HEgAi1rGilokP8GNQxFIcDftyRxp60XO55ZwN2B1w9Ip6bxnb29BJbNQULpW5LnzZDR7wD4Tevg5evSy9/LK+Y53/azfgnf+bBDzfzemZ/DjoiCbfksuzcdOb8dhjDOtX8YuOuSd0BeH/NQY7k6IVda1NYUsa0l1dx9n8Wc9c768k4XuTpJYlIK1KficcNcdmQOAbFh3G8qJSnyrMUpeGcJcgD4kIJ9vNQoC4z0Ww1Cbnlii7vW+jqISe5qZC0yuwrWHj6cpYib//MJDY0VVkpHCwfmKJgYeOd/ZfGlyMXHIP3fgtfP2ACeD3PNWXHncY0fV0WC0T2hPH3wfTv4YFdcPHz0OsC8AszZcwWDb5orZylyJ9sOMSMN9aSW1TKyM7teeyS/lj0fW0SBQuldsm/wKInzP4F/4GI7i67dGL6cR79dAtj/vkjT32/i/TcImJC/HjovL6ET7oHgPCtc807TLUY1aU9wzu1o7jMzitLEl22NqmDwwEf3AyzB0JWcpMu9Y8vt7MpOQuALzenMOWZJXy1OcUFixSR011DJh7Xl9Vq4S8X9gXgg3UH2XLQhU3a25AVe48CMKabh0qQQf0KWwPnkJPUrXX+X69Bsg/B6+ebMuSQuJN7m8npo+tZJshz/AgcWN7066Vth5I8U94c2bvp12urfALhkv+Z/fVvwp4f63de8hp48Qz49UuwesPUJ+CadyHATdnpQVEw9Hq45h344wFT5iyt1uS+MQD8si+T/Rn5xIX5M+e3Q/HxUqirqfQnKLVb+jQ4ymDAlTD4miZfzuFwsCoxgxlvrOXspxfz9qokCkvs9IsNYfa0wSx56CxmntkN/1E3gU+wmUi2t/Z/ZCwWS0V24fzVScpMaw4b3jYlH1kH4Kv7G/0f/E82HGT+6iQsFvjzhX3pFR1MRl4xd76znlnz13FU30sRqUVjJh7X17BO7bhsSBwOB/z9i204XBXEaCMcDgeryjMLx3o0WKjMwhYvohdYvaAou7LHYFNkJcG8803pYmgC3Pw1+Ldr+nWlZfLygT4Xmn1XTEV2liB3HN7wcleprvO4+k9Httth+bPw+rmQnQRhnWD6d2bopTLCpJ56RgcR394fgAAfG6/eOJzwINdWQ7ZVChZKzUoKIHGR2R93b5Mv98u+TC7533KufnkVP+w4gsMBZ/eOYsGto/ny7vFcOiSuMvrvF2Le7QFY+UKd1z2zZyQD4kIpKCnjteX7mrxOqUNOCnz3SOXnu7+HrR81+DK7juTyp49N2dE9k3owfXwXvrh7PPdM6o6X1cLXW1KZ/PRiPt90WC/URaRCUyYeN8Qfzu2Nv7eNtQeO8YWynRvkQEY+h7ML8bZZGF5LC5Fm4exZqB5ULZeXjwkYQtP7FmYmmozCY/vN9/zmr6Fd56auUFq6fuWlyDs+N2XETZGsibguVVGOfBC+/3PNx+QdhXeugoV/AXsp9LsMZi6FuGHNu1Zp9SwWC7eM60KovzfPXj3EZW8gi4KFUpt9S8wkwZCOlRPrGiG/uJS/fb6Nq15ayeaD2fh6Wbl2VAI//v5M5t40gjHdwmvuJTDqdjP9au+PkPZrrdevml345ooDZBeUNHqtUgeHw0wPK8qG2KFw5h/M4988BHkZ9b7M8aJSZr69joKSMib0iOCes81Uax8vK/dP6VWRIXQsv4R7Fmxg5tvrNGhARCgts/NQEyce11dMqB+zJpqJek98vYOCYhf0w2ojVpRnFQ6Jb4e/jwezc1SG3DpULUVurKO7K0uPw3uYQGGYpiC3CV3OhIBwyM+A/Uuadi0NN3GtauXIb5xcjrxvKbw4HvYsBC8/uPAZ0xvfL7T51yqnhZvHdWHjXyZX9C8U11CwUGq261uz7Tm10WngqxMzOO/ZpcxbsR8wE4lW/HES/++yAXSLDKr75HadofcFZn9V3dmFk/tE0zM6iNyiUt4sv5e42PZPYedXpmTokudhwgMQ2cf8B+37R055OpisoD98tJnE8h5js6cNPmliaf+4UD67cxz3ndMTL6uF77YdYcozS/h0wyFlGYq0Yc/9uJsPmjjxuCFuPaMrcWH+pGQX8tKSvW67z+lmZaIJFnq0X2F+JhRmmX1ll7VsFUNOtjTu/LQdJlCYm2L+T3Lz1xAS67r1Sctm84I+F5v9ppQi56aa9jpYIG64S5YmmHLkkVWnI+eYYTSL/glvXmz+3kb0hFt/guG3qOxYmkzDTFxPwUI5mcMBu74z+73Oa/DpzmzCaS+v4kBGPrGhfrx5y0j+ecXAhvUPGH2n2W5+r87sNavVwp1nmezCucv3kVfUxFIEqS4/E75+0OxP+L3JNPXygYv/C1hg04J6NTB+Y8V+vtqcgpfVwvPXDq31Z8HHy8rvzunB53eNp19sCFn5Jdz73kZufXOtpl6LtEGrEjN4/uc9APznqkFNmnhcX37eNv50fh8AXly8l0NZBW6/Z2vncDgqJiF7NFjoLEEOijHZLdJyNSWzMHULzLsA8tIgegDc9KUZWiBti3Mq8o4voLS4cddwliBH9zOtkMR1zvmredMm5yB8eR+8eYkZnumww+Dr4LZFTapgExH3UrBQTpa6xYy89w6AzhMadGpN2YTf3ncGZ/SMbPg6EkZD7BAoLYS1r9V56IUDY+kSEUhWfgnzVx9o+L2kdt8+DHnp5l37Cb+vfDx+hCkXB/jyXijOq/US65OO8X9f7wDgT+f3YVinUzcd7xtrBhc8MKUn3jYLP+xIY/LTi/lw3UFlGYq0EcfyirnvvY3YHXDlsI5cNqRjs937/AExjOzSnsISO//8pvZ2GGLsSTvO0eNF+HpZGZIQ5rmFqAS59XBmFmYm1vl/iJMcWg/zLjTVDbFD4MbPITDCPWuUlq3TOAiKNtnEzl7rDaUSZPepWo689UPYvxS8A+Gyl+DSF/SGjkgLp2ChnMyZVdh1InjXr3F8XdmEIX7ejVuHxVKZXbjmFSitfUKuzWrhjvIeU68s3UdhiXpMucTuhbD5XcBiyo+9TsgGnPQohMabKYQ//78aL5GZV8xd89dTUubgggEduHlc53rf3ttm5a5JPfjy7gkM7BhKTmEpD3ywiVvmrSElW5k+IqczZ+uClOxCukYE8reLmzf7wGKx8NeL+mKxmAnMa/ZnNuv9Wxtnv8Lhndvh69US+hVqEnKLFxQFgVGAw5QU10fyGpOdVJgFHUfCDZ9BgAeH6YhnWW3Q9xKzv62RpcgabuJencfD6FlmP7o/3L4YBl3t2TWJSL0oWCgn2/WN2fY8t16Hr0rM4NzZLsomPFG/SyE4Fo4fOWU/ksuGxBEX5k96bhHvr01u+r3buqJc+OJesz96FnSsoY+LbzBc8LTZX/WCebe/ijK7g9+9u4HD5S/2/3nFgEb1k+gVE8zHd4zloXN74WOz8vPOdKY8vYT31yQry1CkJbOXmdYWjTB/dRLfbz+Ct83Cc9cMIdDX9cNMTqVfbChXjzDDEv7+xTbsdv2+qY2zBHlsNw9neGkScutSUYpcj76FB1bAW5dCUY7JKLv+Yw1E+P/t3XdYVNfWwOHfDL0jRRQpoqJgV7Bg7zVGo0nUFM2NSTSWRE015n7x5iYxPSaxxzRvbCkmMfbeO/ZeUEEEEQtV6pzvj00RpQsMZb3PM88cTpnZA4eBWWfttUR2V+QzqyC1iOVqUpMg4ohalszC0tP7I3hxi6pP6OJr7NEIIQqp7P/zFuVbfBSEB6tl31757pqYksana89mBQndHSz5eEjTkgkSZjIxg9Yvwqb/wN5Z6kpUHsEmMxM9YzrX4d9/n2Tu1osMa+WFuanEw4tt439UjZFqtaFbPk1M6veCxo+r6QUrXoGXtqifGzBz8wV2nI/G0kzP7GdaYlfcLFPA1ETP2C716Onvxhu/H+NI2B3e/OMYK49HMH1wE2o5WhX7sUUlFrIVYsKh+VNSPLusRRxVzQdSEsDCXl1csMy4z/w6a13O9WGJZvy18iJ1dBaM6tqMxq5mKuhohJ/ha70asPJoBCfCY/k9+CpPtpJOq/czGDT2XlLBwrZ1jFivENSUVpBpyBWFW2O4uBmuF1C3MGQrLBkOqYmqC+7wJTKFUSiebVRiQdw1uLgpu0FiYUQchfQUsHaRCwylSaeDWi2NPQohRBFJsFDkdH69uq/ZHOxr5rnb3pCbvPn7MUJvJQIwvLUnU/r5F3/KcX4CnoPtn6mrzpd3gk/edRSfCPTkm80XuBaTxJ+HrzK0lVfJj6cquLJbTf0GGPBNwf+Q9/lY/YN2/Tjs/hY6Tmb7uRvM2HQOgA8HNcGvRskUjfZ1s+OPl9vx/c4QPl9/ju3nbtD7q+1M7e/PsFae0glLZNv9Lax/Vy3HXYNObxh3PFXNsV8hJV4tJ8eoW2zhDvUEfjdF/ZeyM+OmNwUbV7CrAXY1c97b1sj+2toZ9CV3ocjF1oJXe/jywarTfLruLH2b1HioCx+V0enIWO4kpmJjbkJTDyNneknNwoqlRhN1n1+Tk/MbYdnTqoZ1vR4w9BcwkwuEIoNeD40eU0kFJ5YXLViYVa+wjVxQFEKI+0iwUOR0NmMKch5dkMskm/B+1k7QbDgc/F5Ndc0nWGhpZsJLHevw4erTzNl6kSEtPTA1kezCIkm9C3+PV8stR0CdzgUfY+sKvafDX2Ng68dc9+jNq0vD0TQY3tqLIQEl25TARK/jpU516e7vxpu/HyP4ym2mLD/OqmMR/GdgI+q62pbo84mC/XP0GqG3EnGxNcfF1kLd7CxwsTUv+/plBoMKEu6dlb1u8wfg6gf+A8p2LFVZyDZ1/8hXqllWciwkxaoSB8lx6uvM+6Ts5SsR17kbdwdH/V2qW6SgT44DNDCkQVyEunE47+fVm94TPMwlsJh5b1Wt0B8ORwTVZvG+UEKiE5i55QJT+vo/9LenMsmcgtzaxwmz/P7mGgxw5BeVJdb9/1TmeklKSYD4SLUsWUIVQ2Yn1Osn1flxf6D/7Br4dYTK/mrQD5746cH6yUI0Hqz+5p9dAymJYG5duOOuZtYrlCnIQghxPwkWimxpyXBxi1qu3/uBzbllE77Tz79sMizavqyChWfXwM2L4Fw3z12fauPF7K0XuHwzkVXHIxjYvFbpj68y2fYJ3LqoPlD3/G/hj2s2DI4tg5At3Fg8mjuJb9HI3ZH3BjQstaHWdbXl19FB/LjrEp+vP8vOC9H0+mo7T7fx4tXuvjjbygeKshB85TYTluQdvLGzNMXV1gLnewOJtha42GV/nbn9oevSpSXDX2PVtHiAXh/AnTDYPw+Wj4ZRPtk1skTpSYhWmcYAfgPUBYVCWHcyktEnVCmMhc+3pkZ9VxVASE1QAcWEKIiLzAga5nKfcEMFFWOvqlt+fHvD0P8VKvBgbqrn3Uf8ef6ng/yw8xLDW3lR20WmQGbKbG4SVDefKcjXT8LKSdmZPPFRMPKfks3muX1Z3Vs6StOLisKlPpiYQ0ocxITmDCCf+ht+f179TjccCIMXgKm50YYqyrFaAeDopRrunV+vap4XRNOkuYkQQuSjWJ/KZs+ezWeffUZERASNGjVixowZdOyYe7bXzp07eeuttzhz5gyJiYl4e3szevRoJk2a9FADF6Xg8k71gcyuppqGnCExJY1P1pzh5z1XgDLKJryfi6/6YHd+HeybC/0+y3NXGwtTRnXw4fP155i5+QIDmrqj18vUgkK5dgR2faOW+38JVo6FP1angwEzSPm2DY1TjzPScgfPPz0NS7PSzSoz0et4oWMduvu78eGq02w8fZ2Fe66w/FA4Y7vW5fn2PqU+hqpuwY4QuugPU9PJgQjnNtyMTyE6Ppno+GRS0zXiktKIS0ojJDqhwMeyMjPBxc6c2s42tKrtRKvaTrTwcizczzApVk1Vu7Qd9GYwaDY0fRLS0yD6bHbNqxc3Fzp4JYrp0nZ1X71Rob/XETF3eeuPYwC81KlO9t8YvT67vqFDARd/0lIKDijGRcDd2+rvyarX4NFvCxWw6tqgOp3ru7Lt3A0+XH2a70bk0vSpCkpLN7D/kuoUnWtzk5QEdRFqzywV9DGzAS0dLu+AE39Ak8dLbjAyBbniMTED1waq1Ezkiexg4fHfYflL6lxp8gQMmgsmkuMg8qDTqanIu75WXZELEyy8c0U1UNSbgXvz0h6hEEJUOEX+q7ts2TImTpzI7Nmzad++PfPmzaNv376cOnUKL68H68PZ2Ngwfvx4mjZtio2NDTt37mT06NHY2Njw0ksvlciLECXk3Fp179sr64OTUbMJ7xc0Vn24O7wIuk7NN5A1ol1t5m0P4XxUPOtPRdKncd71F0WG9FRYMV79Y95oMPj1K/JDrAqz4FDyEP5ttoipZosxM58MFHIqyEPycbFhwchA9ly8yYerT3EiPJZP157llz1XeKNPAwY2qyVB41IQejORkFMHWWP2Ofp4DXyegGGfgVU1NE0j9m4aNzICh9HxyUTHJRN9TzDx3uWkVAN3U9MJu3WXsFt32XE+GgAzEx1NPRxpVduJNj5OtPSuhoPVfe9BcZGw6HH1gdPcVmWM1e2mtpmYqqlr33VXWbO/PgsjVkiGSmnKDBYWpowBqnP6xKVHuJOYSpNaDrzeq0HxntfUHBw81C0/FzbCoifg8P9UzbQ2owt8aJ1Ox78f8WfnjGg2nLrOjvM36OgrQefj4THEJ6dhb2mKf837atOeXQOr34CYMPW1/wBV4/bIEtjygSoXUL+3CgSXBOmEXDG5NVHv3ddPgP8jcGSxyhBHg+ZPq4C+Xi76iQI0GqyChefWQ3I8WBRQkiYzq7BmM6mBKYQQuShysPDLL79k1KhRvPDCCwDMmDGDdevWMWfOHKZPn/7A/i1atKBFixZZX9euXZvly5ezY8cOCRaWJ5qWHSys3wdQ2UIfrDoNGCmb8H4+nVXXvOsn4NDP0P7VPHe1tzRjZFBtZm65wMwtF+jdqIY0vijIrq/VP+tW1aDvp0U+/OKNeN78/ShJ6X14wfEQNRNOqw+JQ/9XCoPNW1BdZ1aM68DfR8P5bO1ZrsUkMWnZUb7feYl3+vnnnvkiiu2HXZfopduPXqepFcd/U1nKA2eiq9cDB2szHKzNqFc9/3/aNU0jISWd6LhkbsQnczoilv2XbrH/0i2i4pIJvnKb4Cu3mbvtIjod+NWwp42Pyjxs63AL5z+HqelHNq7w9O8PZglYVYPhS2FBDwjdA6smwaMzpaB5abmUUa/Qp1Ohdp+95QL7Lt3C2tyEb4a3KP1O9vV6QM/3VbBq7RQ1FbJu14IPq27HiCBvftx1mf+uPMXqVzpW+bq4e0KyuyCbZF6QibkKa96CMyvV1w5eakZAA/X/Be0mwJFFKri37RNVLqAkZHVCrlMyjyfKRmbdwsjjEPwT/DMR0FSDu/5flWjDIlGJ1WymfvdvhajPNAVlLd/b3EQIIcQDivTXNyUlheDgYHr16pVjfa9evdi9e3ehHuPw4cPs3r2bzp3zzjZITk4mNjY2x02Ushtn1AdtU0uo04W1JyKyAoXDWnmyblIn4wYKQX2ob/uyWt43X00tzMfzHXywMjPhRHgsW8/dKIMBVmA3zqkPbAB9PinyFM3ElDRe/iWYhJR0An1ccX1qHuhM4PQKOL2yFAacP71ex2MtPNj8ehfe7NMAWwtTToTH8tR3+3jh5wNciIor8zFVRjGJqfx6MIzeJgfVijZjwLmemub5yxBVnyw5vlCPpdPpsLUwpbaLmn48Iqg2M59qyb53urPtjS589nhTngz0oLazNZoGpyNi+Wn3Zb5b8iv80BvuhBJtXovVrRdyxcIXTdMefBLX+vD4D6DTw+FfYO+cEvxuiCx3wtSHNZ0JeLcvcPfgK7eYsek8AP8d2BifsqoFGDReNc/S0uG351Q93EKY2L0+1azNOHc9nsX7Q0t3jBXAnnvrFaanwe6ZMLO1ChTqTaH9RBi3NztQCGBmmX1Rau8ciDpTMoORacgVU2Yd2Qub4J9XAQ1aj4ZHZkigUBRe5lRkUF2RC5IVLJTmJkIIkZsi/QWOjo4mPT0dNze3HOvd3NyIjIzM91gPDw8sLCwIDAxk3LhxWZmJuZk+fToODg5ZN09Pz6IMUxRHZhdkn06cuJHKpGVHARgR5M3HQ5oaZ9pxbho/rjKHYq/Cqb/y3dXJxpxn2qqp8d9uOp978ECAIV1NP05PUVPQmz5ZpMM1TePdP09w7no8rnYWfPtUC0xrNcvO/Fz9OiTFlMLAC2ZpZsLYLvXY9kYXRgR5Y6LXsfF0FL1n7ODdv44THZ9slHFVFov3h+KUGklj/WU0nR46vQGjd6igIcDBH2BuBwjdW+zn0Ol0eDvb8ESgJ58+3oytb3Rl/zvdmfVUSz5oGM5Siw9x1sVxxFCH3rHvMnbNbTp/tpU2H21i/OJDLNxzmcv31kr07ZGdxbR+qpqOKkpWZlZhrZZgaZ/vrjF3U3llyRHSDRqDmrszuGUZNqTS6VQwolYgJN2BpU+pupcFcLA2Y3LGNOkv1p/jdkJK6Y6zHEtJM3DgsqpX2M32Cszvon6vUhPAsy2M3g49/wPmuQSA6/eCBv1VHcPVr6sZDg8rM7NQpiFXLG5N1H3aXXXfbgL0/UQyv0XRNRqs7i9syP9/z+Q41XQJJLNQCCHyUKzLdfdP59Q0rcApnjt27ODgwYPMnTuXGTNmsGTJkjz3nTJlCjExMVm3sLCw4gxTFMW5dQDEeHZn1M8HuJuaTkdfF/7vkdLrZFssZpbQKiPQvOIVOL8h391f7FgHc1M9h0LvZE2VEvc5sEBdXTW3hUe+KvI/50v2h7H8cDh6HXw7vAXV7SzVhs5vglNdlWW2cVrJj7sInG0teH9gY9ZP6kTPhm6kGzR+2RtKl8+2MmvLBe6mpBt1fBVRSpqBn3ZfoqdeZRXqvILAxgXMrdWHvBF/g72Hmmb4Y1/Y8J7qVFwCqttb0j99E89cehtLkkn16U7ME3/wROeWtPRyxMxER1RcMiuPRfB/f5+k+5fbmL/9YvYFg7ZjocUzoBngt+ch+nyJjEtkyKxX6JN/vUJN03jnz+OE37mLl5M1/x3UuOzLRZhZwrBFqrHXjTOw/EV1AaUAw1t54lfDjpi7qczYeK4MBlo+HQm7g3lqLJ9b/YjXn4NUB2yraqrG3L/WZE8vzUuf6WpGw+UdqinBw0hPVdOfQaYhVzQ2ztk/s46vQ8//SqBQFI9bI1VWIj0FzqzOe7/wYPU/gIMX2EtdcyGEyE2RgoUuLi6YmJg8kEUYFRX1QLbh/Xx8fGjSpAkvvvgikyZNYtq0aXnua2Fhgb29fY6bKEUJN+GqKvI78ZAb12OTqVfdlllPtyyftZjaTYA6XVTmwuKhcGhhnrtWt7dkaKDKTJ215UIZDbACuX0FNv5HLff8T8FNAe5z/GoM01aoK7Nv9PajbR3n7I1mVvBoRmflgz/AlcKVKihNdV1t+W5EIEtfaktTDwfik9P4bN1Zun2xlT+Cr2IwSPZpYa06fo3rsck8Yn5IrfB7JOcOdbrA2N3Q7Cn1D/muGTC/q6pJ9TA0DbZ/Bn+PU9NHmw3H7JlldG5Sh7f7+rF8bHuOvdebJS+2ZVKP+rSqXY10g8ZHq88w5pdgYpNS1YfQ/l+qzKfkGPU+cvf2w41LKJoGIYWrV/jbwausOhaBqV7H18OaGy+D3a6GChiaWKg6V5sLrp9naqLPupj2y75Qzl2vgqUNNI1bexayyeJ1Htc2oMtsRjH+ILQcUbjpo9W8oeNranndVJXtU1x3QtV7gqmV+pmKiuWpX+GZ5dD93xIoFMWn02VnF+Z3ASKzuYlnq9IfkxBCVFBFigSZm5sTEBDAhg05s7k2bNhAu3btCv04mqaRnCzT/8qNCxtAMxBmXpctkeZUszbjh5GtsC8vU4/vZ24DT/2WXWtqxQTY8lGeU5hGd66DqV7Hrgs3Cb4iAYEsmgYrJ6qgq3d7CHi+SIfHJKby8qJgUtIN9PB3Y3SnXDI5aneAliPV8opXIDXp4cddAtrWceavse35elhzajlaERGTxGu/HeWRb3ey+0K0sYdX7mmaxnfbL+FELC00VdsUv/4P7mjpAI/NgaG/gLULRJ1UAcMdXxRYczRXhnQ1XTEzmNNhMgyaAyY536uszE0IquvMqz18+XV0EP8d2AgzEx3rTl7n0W93cupaLJhaqHE5eKoOyb89V7wxiZyiz0F8pMoWy2dq14WoeN7LuNAwuVd9WnhVK6sR5q5WAAycqZZ3fgnHfy/wkHb1XOjdSGUqT1txsmpdbIg+Dwsfpc+593DVxRJjUweeWwWDZqsM46Jo94qaNhwXAduK3lwry731CiXYVPG4+EK97sYehagMGmcECy9uhsRbue8jzU2EEKJARU4bmzx5MgsWLOCHH37g9OnTTJo0idDQUMaMUTWqpkyZwogRI7L2nzVrFv/88w/nz5/n/Pnz/Pjjj3z++ec888wzJfcqxMPJ6IL8V2ITzEx0zHs2EC9nayMPqgCm5ipI0OlN9fW2T1SmUXrqA7t6VLPOqoMl2YX3OLJY/SNlagkDvilSEXGDQWPyr0e4evsunk5WfPFEM/T6PD6c9XwfbN3g5nnY8XkJDf7h6fU6BjavxabXOvN2Xz/sLEw5FRHLUwv28fxPBzhfFTOFCmlPyE1ORcTSx/wIegxQo4nKEMqL/wAYuzejPlkqbHpfTU0uZEMJQAWafxupps2jU80RerxXYFBAp9PxbFBtfhvTjlqOVly+mchjs3fx28Ew1chn+BIws4GQrarWmng4mVmFnm3UFN9cJKel88qSw9xNTad9PWfGdKpbhgPMR9Mns2ut/j0Orh0u8JCp/Rpibqpn98WbfLmhCkxHTk1SF+fmtINL20nSzPg09UluPrtJXRwqDjNLVboAYO/s4jc7uZ0RLJR6hUJUba4NoHojVQ/1zKoHtxsMcPWAWpbmJkIIkaciBwuHDh3KjBkzeP/992nevDnbt29n9erVeHurD4oRERGEhmZ3BzQYDEyZMoXmzZsTGBjIt99+y8cff8z7779fcq9CFF9aCqlnVabopvSWTB/clNY+TkYeVCHpdNBtqgp06UzgyCJY9ESuBepf7lIPvQ42n4niRLhxmm2UK3HXYd0UtdxlCrjUK9Lh87aHsOlMFOameuY8HYCDdT5ZqFaO0C8jSLjzq+yC0uWEpZkJYzrXZdubXXmuXW1M9To2n4mi94ztfLL2DClpBmMPsdxZsEN9KB/hmDGl+P4pyLmxdVVTPQfNAQt7VfpgbgfY/13BjQ3u3oFfBsPpf8DEHJ74EdqMLtKYm3s6snJCBzrXdyU5zcAbvx/j7T+OkeTcEAbPUzvtmwvBPxXpccV9Mpub1Mm7XuEna85yKiIWJxtzvnyyed4XGoyh+3uq0VNaEix5Sr1X5sPL2ZqPHlPNGWZuucBfh8PLYpTGcXEzzAlSF+fSU7jt3omeKZ+y3GYYPm4PmRlavzc06Kc+3K95o3jNTqQTshAiU+OMrsi5TUWOPqean5hZg1vjsh2XEEJUIMUqSDd27FguX75McnIywcHBdOqUXZfop59+YuvWrVlfT5gwgRMnTpCQkEBMTAyHDh3i5ZdfRl+ELCZRes4cWI9ZWjzRmj1BnXryeEDRataVCwEj4allGdlBW+DHfhB7LccuPi42PNLUHYDZWyW7MKtDcc1mEDS+SIfuuXiTz9apzI9pAxrRuJZDwQc1fFQFlAxpatp4IRoIlDUnG3OmPdqI9ZM60auhGwYN5my9yGOzd0mW4T0uRMWx+UwUNrokGiSo5iaFChaCCvA3fwpe3q3q2aUmqnPxl8EQk0eQJSZcZSFe2aWCjM8sh0aPFWvs1WzM+fG5VkzqUR+dDpYeCGPInN2EVu8O3d5VO616DS7vLNbjV3mGdNWoAvJsbrL5zHV+2KWCOp893hQ3+9yzD41GbwJDFqgC+XHXYNkzBTbmeTzAgzGdVXbkm38cq5zlLrZMh/89proN29aAJ35igdenhGluBNV1LpnGNJnNTi5tL16zk8xOyBIsFEJk1i0M2QYJ95WXyZyCXCvggTImQgghsknErgoLvZnIwfWqK/U5+yDe6O1v5BE9BN+e8K9VYFNddWNc0BOiTufYZVxXlT235kRk1Q7+nPobTq8AvSkMnAUmpoU+dMuZKF5ceBCDBoNb1mJ4a8/CP2+/z1WwJzwY9s8vxsDLRh1XW+aPCGTuMy2pZm3GyWuxPPLtTn7cdalq1STLw/c7VaBngucVdOnJUK12wR1P7+foCc/+DX0+UcGBzIylY7/mzCiKOgPf94SoUypA8a814NPxocav1+t4tYcvP/+r9T0/3x1sdH4WGg9RAe1lz8Ltyw/1PFVSxFF1EcLCHmo2f2BzVGwSr/92DIDn2tWmu3/+jdGMxtIBhi1R91f3w8pJBWa6vdm7Ab0aupGSZmD0/w5y9XZiGQ22DFw7DNszagm2Hg3jD0Cjx9gTomqBBdV1zufgIqhWW9UhBVj3LiTHF+14mYYshMjkXFddENfS1f+898pqbiJTkIUQIj8SLKyiYpNSGfXTftqnq5odAT2Hl6+pYMXh3gJe2ADOvhB7Fb7vDZd2ZG1uUMOO3o3c0DSYvbUItdIqk8RbsOp1tdxhkqo1VwiaprFgRwijfj5AfHIarX2c+GBQ46Jlk9jXVB2XATb9V3ViLsf6NK7Juomd6NJATVv9zz+nGPnjfiJjykeTFmO4GZ/MH4dUBuDjtkfUSr9HitdMQK+HtmNg9A5wb6mCTMtfVHUJE25C6F74oTfEhqssrxc2QI2Smy7Uqb4rq17pSHNPR2KT0njhf8F8af0KWs0WcPcWLBn+cJ1Zq6LMKci1OzxwEULVOT3KrYQU/Gva83ZfPyMMsAhc6sHjP4JOr0pc7J2T7+56vY6vhjbHv6Y90fEpvPDzQRKSK0HDHEM6/DNRdTRv8gT0+xQs7YlPTuPoVVXSo11JBQtB1YysVltldW4vQrMTg0GmIQshcsrMLjxxX6ayNDcRQohCkWBhFZSWbmDC4sOkR5/HR38dTW+GRYMexh5WyahWG0atB68gSI5R0xvv6Wo5vqsvACuOXuPKzQQjDdKI1r8LCVEq+NLpjUIdkpJmYMry43yw6jQGDYYGevLLqDZYmxc+IzFLy+fAq53qwLxqcvHqUpWh6vaW/PhcK/47sBGWZnp2nI+m94ztrDx2reCDK6H/7b1CSpqBlrVscA7folYWdgpyXlzrw6gN0HWqynY99TfMbgMLB0LSHfBoDc+vA0evhx7//dwdrfh1dBDPtasNwDfbwxlneJ10GzeVzbj8JRWEEIVzabu6z2UK8vwdIey8EI2lmZ5vhzfH0sykjAdXDPW6Q6+Mztvrp8KFTfnubmNhyoKRgbjYWnAmMo5Xlx4pWjZy6F5Y+rS6pZSTzMT930HEEbBwgN4fZa0+cPkW6QYNTycrPKqVYEM0M0vVvAhgzyy4cbZwx8VFQHqyeg9xKPn3CiFEBZRZsuTKruz6swk3VcM9AI9WxhmXEEJUEBIsrII+WHWabedu0NvsCAC62h3Aws64gypJ1k7w7F/QcCCkp8Afo1RjDU2jiYcDneu7km7QmLutimUXXtikMmTQqenHphYFHnIrIYVnv9/H0gNh6HXwbn9/Ph7SBHPTYr516PXw6DeqScWFjTkCueVVZjfdVa90pKmHAzF3Uxm/+DCTlh0h5u6D3bcrq6TUdP63R2WDvuF3A11yLNi4lsw0HhNT6PwmvLARXP0g4YZqMFG/L4z4W/1OlxJzUz3THm3EN8NbYG1uwuorOkYlTcKgN4ezq2Hzf0vtuSuVtGS4skct+3TKselI2B0+X6eCPtMGNKJe9Qr096btWGj+tMqs+/1fBXbwruVoxfwRAZib6tl4+jqfrisg2KVpahr+j/1VJu2Zleq2cVrJvYbiir0GmzOCpT3eA9vqWZv2XLwJQLs6LiX/vPc2O1ldyGYnmVOQHTyLVFpDCFGJVfOGWoHq/fvU32pdZhdkl/ql+r+FEEJUBhIsrGL+t+cyP+2+DMCL1TM+xDToa7wBlRYzS3j8J2g7Tn29cZpqXGBIZ0I3Vbvw9+CrXLtz12hDLFPJ8WoqGUCbMYUK8Jy/HsegWbvYd+kWthkZMy90rPPwhexdfFVgCGDtW+oqb1GlJaurxDfOQdgBOL9BBR4PLVQdc6/sVtsSb5VYZlhdV1v+eLkdr3RTnbX/PBxO3xnbsz40V3Z/HQ7nZkIKtRytaJOcERRq0Fc1hCgp7i3gpW2q2UjXd2HoL2BegllL+Xi0mTsrxrenXnVbtiZ4MTnlJbVh55eqlqLI39UDkHZX1Y2tnl3/Ni4plVeXHibNoNG/SU2GtipCndPyQKeDR75SGa5JMbBkmLrPR0uvanz2eFMA5m67yO/BVx/cyWCAM6vgu26qcciVnaA3A/9H1fb981QQ0ZjWvAUpcSr7JuBfOTbtvqgaBpRYvcL7ZTU72QYn/yx4f2luIoTITeOMqciZTZOypiBLvUIhhCiIXH6tQnacv8G0f04B8G63mjjtOaQ21O9txFGVIr0e+nykmimsnQIHv4e4CAKHfE/bOk7sDbnF/O0hTHu0iM0ZKqLN/4WYUDWVM7Praz62nI3ilcWHiUtOw9PJiu9HtqK+WwlmA7V7FU78CVEnYfVr6oNoUoyadpoUA3fv5Pz6/nVpRagbqDNRV4+tXcDG5b5lF7BxBmvne9Y559kdz8xEz+ReDejiV51Jy45w5WYiTy3Yy4sd6/Bar/pYmFaAqZXFYDBoLMhobPKvdl7oD6xWG/wGlPyTmVkWeop8SatX3Y6/x7Xn7eXH+etoO3wJZZzpCrS/x6NzqgseAUYZV4UQklGv0KdTjhqW//f3Sa7cTKSWoxUfDW5SMl1zy5qphQpcz+8C0efgjxdh+JJ8A+UDm9fiQlQ8326+wJTlx/B2tqZVbSdVA/Dkn7DjCzXVHcDUCgKeg3YTwKGWqit74Dv4axyM3Q1W1crkZeZwbp1qCqAzgUdmqL+nGWISUzl5LRYoxWBhtdqqru7W6bBuKvj2AgvbvPfPqldYp3TGI4SomBoOgnXvQOgeiAnPbm7iIcFCIYQoiAQLq4gLUXGMXXSIdIPG4Ja1GFXjvOoQ5uqv/imvzNq+DPbuqv7Y2dXw8wAmtZvN0JBbLNkfyriu9XC1K3hKboV1KyS7+/CAr/P9wKVpGj/uuswHq05h0KB1bSfmPNMSZ9sS/v6YmsOj38KC7uqDc2EyRx6gA0t7sHRUXUutHFUmyt07kBitMhaTY9R5nnBD3W4U8qEtHcDWDbzaQr0eqgablWPW5pZe1Vj9Skc+WHWKJfvDmL89hO3nbjBjWHP8atgX47WUb9vO3eBCVDy2FqYM94iGzRFgbvvAdNPKwMbClG+GNSfQuxofrhpK/fRwehJM6uJhmI3Zpt5LxIMy6xXW6YymaWw7d4N520LYE3ITvQ6+HtYcB6vcg/AVgp0bDFsEP/aF8+tg0/vZDZvyMKlHfS5ExbPmRCTjFu5lbddrOB2elZ0FZ2EPrV5QU51tXbMP7Pk+hGyBmxdg9Zsw5LtSfGG5SEmE1RmNsILGPtBYaO+lm2ga1HG1wc3esvTG0f5VOLpEdSbf/qn6vuRFOiELIXLjUEvVMQ/dA8d/g/BgtV6amwghRIEkWFgF3EpI4fmfDhKXlEagdzWmD26C7u+ZamNlzSq8X8OBYFsDlgyF8IO03jSUvjXfYU2EDQt2hjClr3/Bj1FR7fpG1Wup1wPqdstzt9R0A//390mW7A8F4IkADz54rHHpZct5BED3f8OB71XgycpRBenuDf7l97WFfY5sl1ylpUDiTRU8TLwJCffeZy5nbE+IVl1wNUN2NmP0OTW1WWeipqzU666+jzWaYWNhyvTBTenm58bbfxzjTGQcj367izd6N2BUB5+K3138Ht/tUMGNYa08sQnJmJLr21NlAVZCOp2Oke1q09TDgTd+Ac+kt/FLDOPW94/jNG5jmU2NrjCS4yH8IADrExvw5dc7OBOpOkmb6nW808+fwNqVoDZUrZaq3usfo2DXDHBrDE2fyHN3vV7HF4/50vzaUgYk/I7Tpltqg5WTChC2fjHHRYgs5tbw2Dz4vhcc/xX8+mUX6i8L2z6BO6Gq/l+XKQ9szqpXWFpZhZnMrKDPJ+rv9p5Zqnaka4Pc982ahiyZhUKI+zQarIKFu75W5TIsHVTNQiGEEPnSaVo5b0cKxMbG4uDgQExMDPb2lS9rpzQlp6Xz7IL97L98C08nK/4a2x5nKxP4rK6azvmvteAdZOxhlp3o8/DLELhzhRSLagyNncg5Mz92vtWNajbmxh5dyYuLhBlNVKOX51ZD7fa57nY7IYWXFwWzN+QWOh1M6evHiyVRn7CiMRjU70VCtMpmCdmiGrFEn8u5n7VLduCwbjeiNTve/uMYG09HAdC2jhNfPNmcWo5WZf4SStrJazH0/2YnJnod29/sSq3/dVSdBId8D00eN/bwSt2thBQ++GU1U6+Nx1kXx1GbDoR4P06StTspNu7oLO0wM9FjbqLH3DTjdt+yWcbXFveuM9VjZWaCSSUIKiedXIPlb8MI17nR/u5XAFibmzC8tRfPd/CpFL8HOWycpppmmVrCv1ZDrVympyfFwoEFsHe2ymoGrmuObHYaypOj/42JZSHKOmz+UGXUWVWDsXvBrkbJvo7cXD8F8zqq5iLDl+Za07j3V9s5ez2O2U+3pF+TmqU/psXD4NwaleE94u8c09wB1QDlY2+VST52b46amUIIQdx1+KIBkPGRt15PeKb8N9gTQojSUtj4mmQWVmKapjH1zxPsv3wLOwtTvh/ZSk0nvbxLBUSsqlW9Ar8uvqrj6uInMb92mKUWHzIhZRwTllSjc31X6rnZUt/NDncHy8oRKNs7WwUKPVqDd7tcd7kQFceonw9y5WYiNuYmfDO8Bd393cp4oOWEXp9R09AJXOtD/V7AdLh9BS5uUh2lQ7aqTMRjy9QNHS7uzfmubnc2ujVl8m5T9obcos+M7fx3YGMGNnev0OfS9zvU9L5+TWpSKzVUBQr1ZiqzsApwsjHnsxcH8sfyJB47PpZmCTtpdmpn1vYYzZprmgvXNOeMmwvhGcsRmjPXqUZaHn9qnW3M+XZ4C9rVK4WOsmUgOj6Zn3dfxnX3L4wAtqc2xMXWnH+19+GZNt44WFfgacf56fZviDoN59bC0qfhpa3ZgbzEW7B3jmpQktkIxdGLqw1H03e7J3ERplzcGMa7jzQs+Hk6v6mmPEcchRUT4KlfHwyUlSSDAVZOVIFCv0dyDRRGxydz9rrKGm1bp5QzCzP1ma6avVzaBqf+ejDLMvGWChRC5S+rIoQoOjs3qN0BLu9QX8sUZCGEKBQJFlZi87aH8HvwVfQ6mPl0y+wGFefWqnvfXiXbybSisK0Oz62C3/6Fxfl1zDWbwaLLx1kX0orPDH6kYIaNuQn13OzwrW5LfTdbfKvbUa+6LbUcrSrO9NK7d+DAD2q54+RcP2RuO3eD8YsOEZechkc1KxaMDKyUNfceWjVvCHxe3dJS4Op+lXF4YSNEHodrh9FdO0xP4KilPXutmvJXvD8fL7vOxtON+WBQYxytK17mamRMEiuOXgPgxY4+cOZ7taFOZzWNp4ow0et48vFhnHEzQ3fwB+yTI3FIvY51ehwOukQcdKH4E5rrsenouUE1InDhmsGZcM2JqwYVVLyY6M74JbDqlQ7UdKg42XdXbibw3Y4Qfjt4leQ0A6vMj4MevAL6svORbliaVfK/K3oTGPwdLOgB0WdVwPDxH1RTkgM/QGqC2s+lPnSYDE0ex8PEjOk1rjF+8WEW7LxEveq2DGvtlf/zmJjBY/NhXic4vx6Cf4LAf+V/zMM4vFB1CjW3hb6f5LrL3hA1Bdmvhh1OZZWN7+Sj/oZtnQ5r31FZQffW3s2sV2jnrqYuCyHE/Ro9dk+wsIolSgghRDFJsLCSWncykk/WngHgvQGN6Fz/nuLp59ap+6pSrzA35jYwbDHa6tfRB//Is6YbeZaN3MWSHYbGbEpvwZaw5hwNy9mF0trchHrVVfDQ1y07kFgug4gHFkBKHFRvCL45f9aapvHz7su8v1I1Mgn0rsbcZwNwKelGJpWRqbm6Ql27A/SYpqZ6X9ysAocXN6O/e5t27KSd2U4wgzNnPFn5eUsChryOf6Omxh59kfy85zJpBo3WtZ1o6uEIq1eqDX6PGHVcxuLXcQh0HJK9IjlOdVeMvQoxudxiwzFJT6EGN6nBTVpklti8J5b2/N3XGbfImqUvBWFuWkANTiM7dvUO87aFsOZEBIaM2Vwd3KHRrSsAtO/5GFT2QGEmS3vVEfm7bqpe49f3/G7XaAodXwP/R3PUVX2kqTsXouKZsfE87/51Am9nm4K7CVf3gx7vqW6e66aqQH1p1OWLvwEb3lPLXd8BB49cd9udUa+w1Log56X9q3BkMdy5Ats/y9lcJqsTsjQ3EULkoeFAWP8u6E1zLx0hhBDiARIsrIROhMcwcekRNA1GBHkzsl3t7I23QlQmhN4U6nY32hjLBRNTdI98BQ36wekVcH4DVvGR9NIfpJf+IJjBdRs/gi1asTa5KWvvuJOYks6xqzEcuxqT46GszDKDiLZ093ejX5Maxp16mpKopsIBtJ+Y4wNrarqB91acZPE+lQk1pKUHHw0uxUYmlZ1dDWj+lLoZ0uHa4aysQ+3qQfz0YfgZwkj+bQ3pN6dg0n6Cyhgq5xKS01i0VwWBXujoo4Ji1w4BOvU7I8DCTgVzqvvlvt1gUPXqYq5CTBjEhmcvXz8Fty4ywfwfHgttyfQ1p3lvQKOyHX8haJrG9vPRzN16kT0ZWWUAXRu4MrpzXdokboffURclbKsbb6DG4FwXnvhJ1cHV0sGzLXR6XdUyzeP9/9XuvlyIimflsQheXhTM3+Pa4+1sk//ztHkZzq5RWTF/vqzqJJb0rID1U1V5khpNoPXoPHfbm9XcpIynzptZQd9PM5qdzMxodpLRoCCzuYl0QhZC5MXGBUatB3Q5M5OFEELkSYKF5cALPx/k6u3EHIXxswrhm5rkKIpvkWObHgtTkxzHmep1/OefU9xNTaejrwv/d39dpMysQq+g3LswVjU6napLV7+X+mAfeUx9j86vg/BDuCWcoV/CGfoBmoMLcZ5duejYnn365py4qXEhKp6QGwncTU3neHgMx8NjWH44nADvavzfIw1p5ulonNd1ZJGqq+foBY2zM6HuJKYwdtEhdl+8iU4Hb/fx46VOVbCRSWnRm4BHoLp1eRtd4i0Sz2zk5IoZtOIkbP4PnPwDBnyt9inHfjsYRmxSGrWdrenh7wYHF6gNnq1V/R9RML1efa/s3FT373vFXYcZjWmRfpYWuvP8uAsCvKvxSFN344z1PqnpBlYdi2Dutos5Ohs/2sydlzrXyS5X8M82de/T2UgjNbK6XeHFzarOX62AAmsK6nQ6Pn+iGWG3Ejl6NYbnfzrAn+PaY2+ZzwUEvR4GzYY57SFsL+z+BjpMKrnXELItq/4qj3wNJrn/axgZk0RIdAJ6HbT2MUJ36wZ9oH4fVUplzRvw7F/q+31bMguFEIVQo4mxRyCEEBWKBAvLgZBoFXAqSfWq2zLzqZaYmtw3rS2zXmH9PiX6fJWCXg/uzdWty1tqWtaFDep7dnELusRo7M/+Rgt+o4XeVAVcW/UirW5Prug9OB+VwOGw2yzcfYXgK7cZOGsXg1vW4s3eftRwsCy715GeCru+UcvtXsn64HfxRjyjfjrA5YxGJjOGtaBnQwn6lCprJ6xbPsmZpFYsXTmXd80XUe36CVXrrPWLqlGCZfmrEZlu0Phh12UARnXwUVPsT/+jNvr1N97AKhM7N2jyBBxZxIc1t9Pvmi9v/X4Mvxr21KtuvKyHlDQDi/ZdYcGOS4TfuQsU0Nn40nZ1X6eKBgtB/c0oAkszE74bEcijM3dx8UYC4xcf5oeRgQ/+vb6Xo5eqI/jXy6pLcr0eJfPBNy0ZVk1Wy61eeDCofY89IdEANK7lgIOVkbKj+3wMF7eoRlOZzU5kGrIQQgghRInTaZqmGXsQBSlsa+eK6mjYHeKS0khJTyclzUByxi3lnvuUNAMp6ekkpxpISc+5Te2fnrGPARdbC/7vkYZ4OlnnfKKkWPi0DhhSYcIhNYVKFE56KoTuUVmH59apjrD3qlZb1QWs34tIp9Z8ujGE5YfCATVF+eUudXmpU52yKfx/dBn8+RLYuMLE4xhMLNl4+jqv/XaUuKQ0ajmqRib+NSvf71J5lZZuoPeM7dy+EcHPHn/TJHqN2mDnDv0+A//yVQNw7YkIxvxyCEdrM/a83R2r9Fj4tK6aainvHSXn+kmY0w5Np2eCyw+sDDOnvpstf41rj7V52V/LS05L5+VfDrH5TBRAwZ2NY67CV41Ap4e3Lleppjcl4UR4DE/M3cPd1HSea1ebaY8WMA1d02DZM3BmJVRvBC9tAdOHrDO79WPVOMTWDcYfyPdn+MZvR/kt+CqjO9VhSj//h3veh7FlOmz7GOxrwbj98E0LSIhSXandWxhvXEIIIYQQFUBh42uSWVgOlNlU1YubVaDQuZ582C8qEzPw6aRuvT9UNZLOrVfTlS/vhNuXYf882D+PGl5BfDlyJSODavP+ylMEX7nNlxvOsXR/KG/382dA05qlN+3XYICdXwFwt+VL/LIngsX7Q7kUrTJXA7yrMfeZAFztpJFJWTI10fNOP39G/ZzAkIiR7HziOapve1tNn1v2tGoY0vdTcKhl7KEC8N0OlanzTBtvrMxN4Og6FSis3lDeO0qSWyOo0xVdyBY+9drLvjs9OXc9nneWH+eroc3LtDzAvYFCSzM9U/s35IkAj/wvcIRkTEF2bymBwmJoXMuBr4Y2Y8wvh/hp92XqVbflmbbeeR+g06kSBmH7IOokbPkQer5f/AFEX4AdX6jlPtML/Blm1qws8+Ym9+swEY4uUc1ONv1HBQpBahYKIYQQQpSg8t16UZSsrC7IMgX5oTnVgbZj4Nk/4c1LMHQRtBwB5rYqA3HvbJp5OvL7mCC+Hd6CWo5WXItJ4pUlhxkyZzdHwu6UyrC0c2vgxmmS9NZ02FKHD1ef5lJ0ArYWprzQwYdFL7SRQKGRdPOrTru6zqSkG3j/VHUYuwc6TFbNhs6shFltYN981STFiA6F3ib4ym3MTfSMCMoIXMgU5NITNB4A6+OLmDOkHiZ6HX8ducYvGQ2IykJyWjpj7wkUfj+yFc+29S44E/pSRrCwKk9Bfkh9Gtfkjd4NAHhvxUl2X4jO/wAbFxiQUWZi1zdwZU/xnljTYNUkSE9Rzc4aDc5397BbiVy9fRdTvY5WtY1Qr/BeZlZqSjbA/vnq3qqa1GEWQgghhChBEiysKgzpKgsOJFhY0ixs1TTSR79V9ZQAtnwEty6h0+kY0MydTa915rWe9bEyM+FQ6B0GzdrF5GVHiIxJKpEhxCalsnD3JU7/9h8Afkzpzs00Kxq52zN9cBP2vdOddx9pWDbToEWudDodU/v7o9PBymMRHIpIgh7vwejt4NEKUuJU0f7ve0HkCaON8/uMrMJHm7tT3d5Sdda+sElt9Ctf06UrhXrdwdUPUuIIvLWSNzMCR//95xRHS+miwr0yA4WbzkRhYaoChe3rFaLTraZl1yusqs1NSsjYLnUZ1NyddIPGy4sOEXIjPv8D/PpBi2cADf4cDclxRX/SY7+qn5+pJfT/osDGLHsyuiA383TExqIcTEpp0Dfn/zJOdYw3FiGEEEKISkiChVVFeDAk3gQLB/Bqa+zRVF4tnoHaHSHtrioan1ES1NLMhAndfdn6RheGtPQAYPnhcLp+vpVvNp3nbkrxssmOX43h7T+O0ebDTaz65w8app8lWTPjRqPn+Xtce1ZO6MDw1l7l48OdoJG7A49n/Pw/WHkKTdPUVNTn10G/z8HcDsIPwvzOsOE9FagrQ2G3EllzIgKAFzpmTOkL2aLOZwdPqNmsTMdTJeh00HasWt43l5c6eNGroRsp6QbGLjrE7YSUUnvq5LR0xi3KDhT+8FwhA4UA0echLgJMLFSHbFFsOp2Oj4c0pYWXIzF3U3lmwT4W7AjhZnxy3gf1nq6anty5AuumFu0JE2/BunfUcqc38m0McjclnT+CrzJv+0UA2hl7CvK9+nyszj+QKchCCCGEECVMgoVVxdmMhgq+PVT9PVE6MmtKmVioGpHHfs2x2c3eki+ebMbf49oT4F2Nu6npfLnhHN2/2MrfR8IpTL+hxJQ0lh0I5dGZOxkwcydLD4RxNzWd16xXqR1aPM3/De9GM0/HMq15Jgrn9d4NsjJMVx1XgTn0Jqo78vj94D8ADGmwawbMCVLnURn5YdclDBp09HXBr0ZGsdszGeeVX/8Cs49EMTV9EqxdICYM3el/+PzJZtR2tib8zl0m/XoEg6Hk+5BlBgo3ni5GoBCypyB7tVHTQsVDsTQzYf6zgXg6qZIVH6w6Tdvpm3j5l2C2nIki/f5zwNIeBs0BdHDoZzi7tvBPtnEaJEaDSwNo98oDmzVN4/jVGKb+eZzWH27ktd+OcvFGAuYmeno3qvFQr7NEOflA1ylq2aejcccihBBCCFHJSDfkqmJ2O1UQffB36oOpKF3bP4fN/wVrZxh3AGwezMbQNI2VxyL4eM0Zwu/cBaCllyP/N6ARzXNpenM2Mo7F+66w/FA4cclpAJib6OnbpAYv1oun8cpHVFfSCYfyzRQRxvfVhnN8vek8nk5WbJzcGQvT+6aHn1kFq16HuGvq66ZDofdHql5ZKYm5m0q76ZtISEnn5+db07m+K6Snwee+cPcWjFwpH8hLU2aH11oB8MImTkXE8djsXSSnGZjcsz6vdPctsadKSTMwdlFwVqDw+5Gt6OBbxHNr6dOq1ma3f0On10tsbFVdXFIqfx+5xq8Hwzh2NSZrfQ17S4YE1OKJAE9qu9hkH7BuKuyZCTbVYezeXP/W5BC6D37opZafWw2122dtiklM5a8j4Sw9EMbpiNis9Z5OVgwN9GRIgAc1HcphYDjuOthWl4sZQgghhBCFUNj4mgQLq4I7oTCjiQokvXERrI1cnLwqSE+FeZ0g6hQ0Gw6Pzc1z16TUdBbsCGH21oskZkxHHtyiFm/28cPR2oy1JyJZtO8KBy7fzjrG29map1p78XiAB862FvDbc3DyT2j8ODz+fWm/OvGQElPS6PLZVqLiknmnnx8vdcqlw3ByHGz+APbNAzRVwL/XB9D86VL5UDxv20WmrzlDAzc71k7sqLJSL22HnweAlRO8fh5MZDp7qYm/AV81gvRkeH49eLXht4NhvPH7MXQ6WPh8azr6uj7009wfKFwwMrDoj2tIh0/rQNIdeGETeAQ+9LjEg05HxPLbwav8efgqtxNTs9a38XHiyUBP+jWpiZUuFeZ3gRunwf9ReHJh3u8P9/5dav4MDJqFwaCxN+Qmyw6GseZEJClpBgDMTfX0aVSDYa08aVvHGb1eAnFCCCGEEJWBBAtFtv3fwerXwasdPL/G2KOpOq4ehAU9AE11Ta7bLd/dr8cm8enas/xx6CoAVmYmWJrpsz4kmuh19PR34+m2XrSv65L94e3mRZgZCJoBxuyCGo1L81WJEvLrwTDe/P0YdpambHujK0425rnveDUY/nkVrh9XX9fuCANnQrXaJTaW1HQDnT7dQkRMEp8+3pQnAz3VhjVvwb65KkA5aHaJPZ/Iw4oJcGihCvoM/R8Ab/9xjKUHwnCyMWflhA64OxY/s0sFCg+x8fT14gcKAa4dVgEqC3vVDV6CyKUqOS2dTaejWHYgjO3nb2SWwsXOwpRHmrnznE8M9f8ZhM6QCo/Nh2ZDc3+gnTNg43tg5cT1kTv57VQivx68Suit7NqofjXsGNbKk0EtauFoncd7khBCCCGEqLAKG1+TmoVVwbmMWkb1ext3HFWNRyC0fkktr5xUYLOKzHqGK8a3JzCjnuHtxFRqOlgyuWd9dr/djbnPBtDR1zVnlseur1Wg0LeXBAorkCEtPfCvaU9cUhpfbzyX944eAfDSFujxHzC1gss7YOGg4nVAzcOqYxFExCThYmvBwObuaqWm3VOvULogl4nMRidnVsIt1ZV62qONaORuz62EFMYtPpSV+VVU9wcKvxtRzEAhQEhGvULv9hIoLAMWpib0a1KTn59vza63uvFaz/p4OlkRl5zGkv2h9F4Ww49mKkBoWP06xFx98EFuX0Hb+jEA31uPIujrI3y+/hyhtxKxszDl6TZerBjfnjWvduS59j4SKBRCCCGEqOIkWFjZJcerqYQA9fsYdyxVUfd/g30tuH1Z1SMrhKYejvw2JojFL7Rh4fOt2fFmV17p7oubveWDO8dGwNElarnD5JIbtyh1Jnod7/b3B2DRvlAu3ojPZ2cz6DARxu4Bew+4fUll/ZUATdNYsDMEgJFB3tn1EyOOQkwYmFlD3a4l8lyiANX9oV4PFfzfp0oXWJqZMOfpAOwtTTkceoePVp8u8sOmpBkYt1gFCs0zAoWd6j/ElObM5iZ1Ohf/MUSxuDtaMaG7L9te78riF9swqLk7FqZ6PozpzSFDPfTJsZyZ+yxbTkdmNUW5GBXHhZ9eRpd2l70Gf/4b3hyDBq1rO/HFE83YP7UHHz7WhKYe0hRLCCGEEEIoEiys7EK2QnqKmrLo2sDYo6l6LOyg/xdqefdMiDhWqMN0Oh3t6rnQqb4rpib5/JrunaV+vl5B4B1UAgMWZal9PRe6+1UnzaAxffWZgg9w8oHB8wEdHFkEx39/6DHsDbnFifBYLM30PN3WO3vDmZXqvl536XZbloLGqftD/4O7dwDwcrbmyyebA/DT7sv8c/RaoR8uJc3A+MWH2HBKBQoXPGygMC0FruxRyz4SLDQWvV5Hu7ouzBjWgv1TezBtUDPmOb9JomaB391DbPvlQ9p/vJnBs3fx2YzPqRezixTNhM/NxjC6c102vdaZX8cEMSTAAytzk4KfUAghhBBCVCkSLKzssqYg95FOgcbSoC80HARaOvzzimoOUBLu3oaDP6rlDpNK5jFFmZvSzx8TvY6Np6+z+2J0wQfUbp/dfXblZLh95aGef8EOlVU4pKVHzrqJpzOChX4DHurxRRHV6QrVG0JqAhz6OWt1j4ZuvNxFNcJ5+49jXIjKJxM1Q2agcP2pEsooBLh6ANLugo2ryoQURudgZcazbb2Z9+pQYjr+HwBvmy3BJu4i50KvMc1MnUdhDV9kyTsjmNLXn7qutsYcshBCCCGEKOeKFSycPXs2Pj4+WFpaEhAQwI4dO/Lcd/ny5fTs2RNXV1fs7e0JCgpi3bp1xR6wKAKDAc6vV8tSr9C4+n4CFg6qMcC+vDsjF8n+BZASD9UbqXqFokKqV92Wp1p7AfDhqtMYDIXoOdX5LfBoBckxsPwlSE8r1nNfiIpn05kodDoY1cEne8PNi6q7qt4U6su5VaZ0uuzswn3zVAfbDK/1rE9QHWcSUtJ5+ZdgEpLz/rmnpBmYsCRnoLDzwwYKIXsKsk8nuQBVDtXsPg7qdseSVP6o8TO/+26ghu42VPOh7uBpmOWXqS6EEEIIIUSGIv/XuGzZMiZOnMjUqVM5fPgwHTt2pG/fvoSGhua6//bt2+nZsyerV68mODiYrl27MmDAAA4fPvzQgxcFiDgM8dfB3Ba8Oxh7NFWbXQ3o9b5a3vzBQ2eDkZII++ao5Q6T5EN7BTexhy92FqacvBbL8sPhBR9gYgaDvwNzOwjbCzs+L9bz/rBLNdHo7udGnXszjTKnINfuAFbVivXY4iE0eQJsqkNsOJz6O2u1qYmeb4a3oLqdBeej4pn653E07cHgcmq6ChSuO6kChfOfDSiZQCFkNzeRKcjlk04HA2eBpSOOt0/gF7ZMre//hZQTEEIIIYQQhVbkYOGXX37JqFGjeOGFF/D392fGjBl4enoyZ86cXPefMWMGb775Jq1atcLX15ePPvoIX19f/vnnn4cevCjAuYwMzrrdwFQ6GxpdixHg1Q5SE2HVa6rbbHEd/h8k3lS1KBs9VmJDFMbhbGvBuG71APh83VnuphRiqrqTDzzypVre9gmE7i3Sc56JjOWPYNU19cWOPjk3Zk1Bli7IRmFqkd1Jfc/MHO8VrnYWzHyqJSZ6HX8ducYv+3JeqEtNV1OP7w0UdmlQvWTGlRwP4QfVsjQ3Kb/sa2a/NwA0HqJqjwohhBBCCFFIRQoWpqSkEBwcTK9eOael9erVi927dxfqMQwGA3FxcTg5OeW5T3JyMrGxsTluohjurVcojE+vhwFfg4k5XNgAJ/4o3uOkp8Lub9Vyu1fAxLTkxiiM5rl2tanlaEVkbBLfZdQRLFDTJ6HpUNU9948XsxpiFGT18QgGz95NcpqBAO9qtPa55/04LlLVpQPw61+0FyFKTuDzYGqpSheE7smxqbWPE2/38QPgv/+c4mjYHaCUA4WgxmFIA0cvdaFClF+Nh0DbcVArEHpPN/ZohBBCCCFEBVOkYGF0dDTp6em4ubnlWO/m5kZkZGShHuOLL74gISGBJ598Ms99pk+fjoODQ9bN09OzKMMUALHXIOIooJN6duWJa33omNGcYs1bkHir6I9x/DeICVPTFJs/XbLjE0ZjaWbCW31VAGjutotExSYV7sB+n4OjN8SEwspJ+Wasphs0Plt3hrGLDpGYkk5HXxcWjAhEd+809rOrAQ1qBYC9+0O8IvFQbJyh2XC1vHvmA5tf6OhDn0Y1SEk3MHbRIaLikpiw+LAKFJqUQqAQIGSrupcpyBVDn4/gxU1g51bwvkIIIYQQQtyjWJWudffVR9M07YF1uVmyZAnTpk1j2bJlVK+e94eYKVOmEBMTk3ULCwsrzjArjmI2J8hX5hRkj0CwLaFaVaJkdJgErn6QGA3r/120Yw0G2DlDLQeNBTPLEh+eMJ4BTWvS3NORxJR0vlh/rnAHWdrDkO9BZwInl8PRJbnuFnM3lVE/H2DWlosAvNSpDj8+14pqNveVKDizSt1LVqHxtR2r7s+uVk1n7qHT6fj0iabUdrYm/M5dun+xjbUnIzE30TNvRCkECiG7uUmdLiX/2EIIIYQQQohyo0jBQhcXF0xMTB7IIoyKinog2/B+y5YtY9SoUfz666/06NEj330tLCywt7fPcavU/hwN33WDXV/DrUsl85hZU5ClC3K5Y2oOA75Ry0d+yW4YUBhnV0P0WbCwV9MURaWi0+n49yP+APwaHMbpiEKWYPBsBV2nqOVVrz8QWDp/PY5Bs3ax9ewNLM30fD2sOe/088f0/s6oSTHZ56PfgId5KaIkuNYH396ABnsfrAtsb2nGnGcCsDTTE5eUpgKFzwbQtTQChYm3IPK4WvbpVPKPL4QQQgghhCg3ihQsNDc3JyAggA0bNuRYv2HDBtq1a5fncUuWLOG5555j8eLF9O8v2So5pKfC+fUQHgwb/g++aQ5zO8L2zyH6fPEeMyUxe7pY/b4lNVJRkrzaQOAotbxyIqTeLfgYTYOdGUXrW70Alg6lNjxhPAHeTvRvUhNNgw9Xnc61222uOkwG7/aQmgB/vKDeW4B1JyMZNGsXl6ITqOVoxe9j2jGwea3cH+P8BjCkgkt9FagSxhc0Tt0fWZRr2QL/mvZ8PawFLbwcmT8igK5+pRAoBLi0Xd27+oNtKT2HEEIIIYQQolwo8jTkyZMns2DBAn744QdOnz7NpEmTCA0NZcyYMYCaQjxixIis/ZcsWcKIESP44osvaNu2LZGRkURGRhITE1Nyr6IiMzGD8Qeh/xeqDpTOBCKPweb/wsxAmB0EW6bD9VOF7557eQekJYG9B7g1Kt3xi+Lr8R7Y1YRbIbDt04L3v7xDBZVNLaHty6U/PmE0b/Xxw9xEz84L0Ww9e6NwB+lNYPB8FUS+dght80d8ueEco/8XTEJKOkF1nFkxvj2Na+UTZD6T2QVZLuqUGz6dwK2J6qIe/FOuu/RuVIM/x7YvnanHmbKmIEu9QiGEEEIIISq7IgcLhw4dyowZM3j//fdp3rw527dvZ/Xq1Xh7ewMQERFBaGho1v7z5s0jLS2NcePGUbNmzazbq6++WnKvoqKzc1OZYiNXwOvn4dFvoV4P0JtC1CnY9jHMCVLBw03vw7Uj+QcOz65R9w36QCFqSQojsXSAfp+p5d3fQOSJ/PffkZFV2OIZyeyp5LycrRnZTr2nfrj6NGnphsId6OCRNcVd2/UVB7b8BcC/2tdm4ajWONta5H1sapLKLASZglye6HTZ2YX750NainHGkTk9XZqbCCGEEEIIUenptELPcTOe2NhYHBwciImJqfz1C+919zacXQun/oaLmyE9OXubozc0fBQaDlJdSzODgpoGXzaEuGvw9O/g29MoQxdFsPRpldFVKwBGbVAZYve7dhjmd1GZp68cgmq1y3qUoozF3E2ly2dbuJ2YygeDGvNMW+9CHXfxRjxn5j9H/9QNRGpOHOjzDwOCGhd84Ln1sPgJle066RToi9X/SpSGtBSY0QTiI+GxedBsWNk+f8xV+KoR6PTw1mUpgSCEEEIIIUQFVdj4mnwaLM+sqkHz4fDUUnjjgup46v8omFrBnSuw+1tY0F19iFvzNlzZAxFHVaDQzBpqdzT2KxCF0e8z1bAkPBgOLMh9n51fqfvGQyRQWEU4WJnxandfAL7acI64pNQCj9l0+jqDZu7i9bjhXNG5U0N3iwFXpheuhMGZf9S9X38JFJY3pubQ5iW1vGdm4UtSlJTMeoXuLSRQKIQQQgghRBUgnwgrCkt7aPI4DP0fvHkRnlyoAkfmthAbDvvmwI994PuMTMI6XcHM0rhjFoVj767qF4KaZh5zNef26AtwaoVa7jCpbMcmjOrptt7UcbHhZkIKs7dezHM/g0Hj203neWHhQeKS02hS2x37pxeC3kxlreZR6y77AdLhzGq17PdIyb0AUXIC/qUuAkUeV/VLy5JMQRZCCCGEEKJKkWBhRWRuAw0HwuM/wBsXYdgSaDoMLBwgPaOelV8/445RFE3A8+DZBlLiYdVrOTOHds0ANNXZ2q2hsUYojMDMRM/bff0A+H7nJa7eTnxgn/jkNMYuOsQXG86haTAiyJtfXmhDtXqtsoPQa6fAjbN5P1HYfkiMVlljtTuUxksRD8vaCZo/pZb3zCq759U0aW4ihBBCCCFEFSPBworOzFIFBgfPU1OVn/4dHp0JzZ4y9shEUej1qjGF3gzOrYVTf6n1MeFwdKlalqzCKqlnQzfa1nEiJc3AZ+tyBvwuRyfw2KxdrD0ZibmJnk+GNOH9gY0xN814a287TmUZp92F30dBWnIuz0B2F+T6fVSHdlE+tXkZ0Kn3iOjzZfOcNy9AXASYWKgLGkIIIYQQQohKT4KFlYmpuWpo0vJZqTlWEVX3g46T1fLqN1WDm72zwZAK3u3BSz6oV0U6nY53+zdEp4O/j1zjSNgdALaejeLRmTs5HxVPdTsLlo5uy9BWXjkP1uvhsblg7QzXj8PG/zz4BJoGpzPrFcoU5HLNpR406KuW984um+cM2aruPVuDmVXZPKcQQgghhBDCqCSiJER50vE1cPaFhChYOQkO/qjWS1Zhlda4lgOPtagFwAcrTzFn60X+9dMBYpPSaOnlyMoJHWjpVS33g+1qwMCMwNLeWXB+Y87t10+qhkmmllCveym+ClEigsap+yNLIOFm6T+fTEEWQgghhBCiypFgoRDliakFPPqNWj75J6QmQI0mUK+HcccljO6N3g2wNNNz8MptPll7Bk2D4a09WfJSW6rbF9DMqEEfaJ3RTfevMRAflb0tcwpy3W6qHqoo37zbQ81mamr5wR9K97kM6XApo5mKT5fSfS4hhBBCCCFEuSHBQiHKG+92EPBc9tcdJoFOZ7ThiPKhpoMVL3WsA4CZiY4PH2vM9MFNsTA1KdwD9HwfqjeEhBvw19jsJjqZwUK//qUwalHidDoImqCW98/Puw5lSYg8Dkl3wNwO3FuU3vMIIYQQQgghyhUJFgpRHvX4D7j6g1cQ+A809mhEOTG+my/vD2zE8pfb83Qb76IdbGYFQ75XjSoubIB98+D2ZRUQ0ulVt21RMTQaBHbuqlzB8d9L73kypyDXbg8mpqX3PEIIIYQQQohyRYKFQpRHVo4wbi88v1Y+pIss5qZ6RgTVpomHQ/EewK0h9P5QLW/4N2z7TC17twcb55IZpCh9JmbQZrRa3jMrO0u0pIVkBAt9pF6hEEIIIYQQVYkEC4UQoipp9QLU7wPpKXDkF7VOpiBXPAEjwcwGok5mdywuSWkpELpHLUtzEyGEEEIIIaoUCRYKIURVotPBwFlg65a9ToKFFY9VNWjxjFreM6vkHz/8IKQmgrWLqnUphBBCCCGEqDIkWCiEEFWNjQs8Nhf0plC7Izh6GXtEojjajgF0qgZl1JmSfeysKcidpMGSEEIIIYQQVYwEC4UQoiqq2w1ePQrDlxp7JKK4nOpkZ4XuLeHswszmJjIFWQghhBBCiCpHgoVCCFFVOXiAha2xRyEeRtB4dX90Gdw4VzKPmRwPVw+oZWluIoQQQgghRJUjbVaFEEKIisqrLdQKgPBgmNUK7D3AOwi824FXO3BtUPRpxKF7wZCmpqc7+ZTOuIUQQgghhBDllgQLhRBCiIpKp4NHZ8LKiSpgGHsVjv+mbgBWThmBwyAVRKzRDEwK+NN/aau6l6xCIYQQQgghqiQJFgohhBAVmVtDGLUeUhLg6kG4shtCd0PYAbh7C86sVDcAc1vwaAXe7VXwsFYAmFnlfLys5iYSLBRCCCGEEKIqkmChEEIIURmY26iGJJlNSdJSIOKoChxe2aPuk2IgZIu6AZiYg3vLjKnL7cG5HkQeV9t8OhnndQghhBBCCCGMSqdpmmbsQRQkNjYWBwcHYmJisLe3N/ZwhBBCiIrHYIAbp1XmYeYtPjL3fV39Ydzesh2fEEIIIYQQolQVNr4mmYVCCCFEVaDXg1sjdWv9Imga3L6ksg4zpy7fClH7+vU37liFEEIIIYQQRiPBQiGEEKIq0unAqY66tXharYuLhJsXwSPQuGMTQgghhBBCGI0EC4UQQgih2NVQNyGEEEIIIUSVpTf2AIQQQgghhBBCCCGEEOWDBAuFEEIIIYQQQgghhBCABAuFEEIIIYQQQgghhBAZJFgohBBCCCGEEEIIIYQAihksnD17Nj4+PlhaWhIQEMCOHTvy3DciIoKnnnqKBg0aoNfrmThxYnHHKoQQQgghhBBCCCGEKEVFDhYuW7aMiRMnMnXqVA4fPkzHjh3p27cvoaGhue6fnJyMq6srU6dOpVmzZg89YCGEEEIIIYQQQgghROnQaZqmFeWANm3a0LJlS+bMmZO1zt/fn0GDBjF9+vR8j+3SpQvNmzdnxowZRRpkbGwsDg4OxMTEYG9vX6RjhRBCCCGEEEIIIYSo6gobXytSZmFKSgrBwcH06tUrx/pevXqxe/fu4o00F8nJycTGxua4CSGEEEIIIYQQQgghSleRgoXR0dGkp6fj5uaWY72bmxuRkZElNqjp06fj4OCQdfP09CyxxxZCCCGEEEIIIYQQQuSuWA1OdDpdjq81TXtg3cOYMmUKMTExWbewsLASe2whhBBCCCGEEEIIIUTuTIuys4uLCyYmJg9kEUZFRT2QbfgwLCwssLCwyPo6s6yiTEcWQgghhBBCCCGEEKLoMuNqBbUvKVKw0NzcnICAADZs2MBjjz2WtX7Dhg0MHDiwGMMsnLi4OACZjiyEEEIIIYQQQgghxEOIi4vDwcEhz+1FChYCTJ48mWeffZbAwECCgoKYP38+oaGhjBkzBlBTiMPDw1m4cGHWMUeOHAEgPj6eGzducOTIEczNzWnYsGGhntPd3Z2wsDDs7OxKdLpzeREbG4unpydhYWHS7VlUKXLui6pKzn1RVcm5L6oqOfdFVSbnv6iqyuO5r2kacXFxuLu757tfkYOFQ4cO5ebNm7z//vtERETQuHFjVq9ejbe3NwARERGEhobmOKZFixZZy8HBwSxevBhvb28uX75cqOfU6/V4eHgUdagVjr29fbk5gYQoS3Lui6pKzn1RVcm5L6oqOfdFVSbnv6iqytu5n19GYaYiBwsBxo4dy9ixY3Pd9tNPPz2wrqC50EIIIYQQQgghhBBCCOMrVjdkIYQQQgghhBBCCCFE5SPBwnLAwsKC9957L0cHaCGqAjn3RVUl576oquTcF1WVnPuiKpPzX1RVFfnc12kyR1gIIYQQQgghhBBCCIFkFgohhBBCCCGEEEIIITJIsFAIIYQQQgghhBBCCAFIsFAIIYQQQgghhBBCCJFBgoVCCCGEEEIIIYQQQghAgoVCCCGEEEIIIYQQQogMEiwsB2bPno2Pjw+WlpYEBASwY8cOYw9JiBK1fft2BgwYgLu7Ozqdjr/++ivHdk3TmDZtGu7u7lhZWdGlSxdOnjxpnMEKUUKmT59Oq1atsLOzo3r16gwaNIizZ8/m2EfOfVFZzZkzh6ZNm2Jvb4+9vT1BQUGsWbMma7uc+6IqmD59OjqdjokTJ2atk3NfVFbTpk1Dp9PluNWoUSNru5z7ojILDw/nmWeewdnZGWtra5o3b05wcHDW9op4/kuw0MiWLVvGxIkTmTp1KocPH6Zjx4707duX0NBQYw9NiBKTkJBAs2bNmDlzZq7bP/30U7788ktmzpzJgQMHqFGjBj179iQuLq6MRypEydm2bRvjxo1j7969bNiwgbS0NHr16kVCQkLWPnLui8rKw8ODjz/+mIMHD3Lw4EG6devGwIEDs/4xlnNfVHYHDhxg/vz5NG3aNMd6OfdFZdaoUSMiIiKybsePH8/aJue+qKxu375N+/btMTMzY82aNZw6dYovvvgCR0fHrH0q5PmvCaNq3bq1NmbMmBzr/Pz8tLfffttIIxKidAHan3/+mfW1wWDQatSooX388cdZ65KSkjQHBwdt7ty5RhihEKUjKipKA7Rt27Zpmibnvqh6qlWrpi1YsEDOfVHpxcXFab6+vtqGDRu0zp07a6+++qqmafK+Lyq39957T2vWrFmu2+TcF5XZW2+9pXXo0CHP7RX1/JfMQiNKSUkhODiYXr165Vjfq1cvdu/ebaRRCVG2Ll26RGRkZI7fAwsLCzp37iy/B6JSiYmJAcDJyQmQc19UHenp6SxdupSEhASCgoLk3BeV3rhx4+jfvz89evTIsV7OfVHZnT9/Hnd3d3x8fBg2bBghISGAnPuicluxYgWBgYE88cQTVK9enRYtWvDdd99lba+o578EC40oOjqa9PR03Nzccqx3c3MjMjLSSKMSomxlnuvyeyAqM03TmDx5Mh06dKBx48aAnPui8jt+/Di2trZYWFgwZswY/vzzTxo2bCjnvqjUli5dyqFDh5g+ffoD2+TcF5VZmzZtWLhwIevWreO7774jMjKSdu3acfPmTTn3RaUWEhLCnDlz8PX1Zd26dYwZM4ZXXnmFhQsXAhX3vd/U2AMQoNPpcnytadoD64So7OT3QFRm48eP59ixY+zcufOBbXLui8qqQYMGHDlyhDt37vDHH38wcuRItm3blrVdzn1R2YSFhfHqq6+yfv16LC0t89xPzn1RGfXt2zdruUmTJgQFBVG3bl1+/vln2rZtC8i5Lyong8FAYGAgH330EQAtWrTg5MmTzJkzhxEjRmTtV9HOf8ksNCIXFxdMTEweiCZHRUU9EHUWorLK7JImvweispowYQIrVqxgy5YteHh4ZK2Xc19Udubm5tSrV4/AwECmT59Os2bN+Prrr+XcF5VWcHAwUVFRBAQEYGpqiqmpKdu2beObb77B1NQ06/yWc19UBTY2NjRp0oTz58/L+76o1GrWrEnDhg1zrPP3989qWltRz38JFhqRubk5AQEBbNiwIcf6DRs20K5dOyONSoiy5ePjQ40aNXL8HqSkpLBt2zb5PRAVmqZpjB8/nuXLl7N582Z8fHxybJdzX1Q1mqaRnJws576otLp3787x48c5cuRI1i0wMJCnn36aI0eOUKdOHTn3RZWRnJzM6dOnqVmzprzvi0qtffv2nD17Nse6c+fO4e3tDVTc//llGrKRTZ48mWeffZbAwECCgoKYP38+oaGhjBkzxthDE6LExMfHc+HChayvL126xJEjR3BycsLLy4uJEyfy0Ucf4evri6+vLx999BHW1tY89dRTRhy1EA9n3LhxLF68mL///hs7O7usq4kODg5YWVmh0+nk3BeV1jvvvEPfvn3x9PQkLi6OpUuXsnXrVtauXSvnvqi07OzssurSZrKxscHZ2TlrvZz7orJ6/fXXGTBgAF5eXkRFRfHBBx8QGxvLyJEj5X1fVGqTJk2iXbt2fPTRRzz55JPs37+f+fPnM3/+fICKe/4bqw2zyDZr1izN29tbMzc311q2bKlt27bN2EMSokRt2bJFAx64jRw5UtM01U7+vffe02rUqKFZWFhonTp10o4fP27cQQvxkHI75wHtxx9/zNpHzn1RWT3//PNZ/9u4urpq3bt319avX5+1Xc59UVV07txZe/XVV7O+lnNfVFZDhw7VatasqZmZmWnu7u7a4MGDtZMnT2Ztl3NfVGb//POP1rhxY83CwkLz8/PT5s+fn2N7RTz/dZqmaUaKUwohhBBCCCGEEEIIIcoRqVkohBBCCCGEEEIIIYQAJFgohBBCCCGEEEIIIYTIIMFCIYQQQgghhBBCCCEEIMFCIYQQQgghhBBCCCFEBgkWCiGEEEIIIYQQQgghAAkWCiGEEEIIIYQQQgghMkiwUAghhBBCCCGEEEIIAUiwUAghhBBCCCGEEEIIkUGChUIIIYQQQgghhBBCCECChUIIIYQQQgghhBBCiAwSLBRCCCGEEEIIIYQQQgDw/3F06VJ9XsBAAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 0.064\n"
     ]
    }
   ],
   "source": [
    "pred = cnn.predict(X_val)\n",
    "plot_prediction(pred, Y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "eb02b6ed-eaf6-4329-adc7-992ce4044941",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "11/11 - 0s - 33ms/step - loss: 0.0166 - mae: 0.0932\n",
      "Epoch 2/500\n",
      "11/11 - 0s - 29ms/step - loss: 0.0172 - mae: 0.0947\n",
      "Epoch 3/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0167 - mae: 0.0941\n",
      "Epoch 4/500\n",
      "11/11 - 0s - 32ms/step - loss: 0.0161 - mae: 0.0916\n",
      "Epoch 5/500\n",
      "11/11 - 0s - 30ms/step - loss: 0.0164 - mae: 0.0928\n",
      "Epoch 6/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0159 - mae: 0.0913\n",
      "Epoch 7/500\n",
      "11/11 - 0s - 29ms/step - loss: 0.0159 - mae: 0.0903\n",
      "Epoch 8/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0152 - mae: 0.0889\n",
      "Epoch 9/500\n",
      "11/11 - 0s - 30ms/step - loss: 0.0152 - mae: 0.0894\n",
      "Epoch 10/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0147 - mae: 0.0876\n",
      "Epoch 11/500\n",
      "11/11 - 0s - 29ms/step - loss: 0.0147 - mae: 0.0877\n",
      "Epoch 12/500\n",
      "11/11 - 0s - 30ms/step - loss: 0.0146 - mae: 0.0869\n",
      "Epoch 13/500\n",
      "11/11 - 0s - 31ms/step - loss: 0.0143 - mae: 0.0863\n",
      "Epoch 14/500\n",
      "11/11 - 0s - 30ms/step - loss: 0.0143 - mae: 0.0861\n",
      "Epoch 15/500\n",
      "11/11 - 0s - 31ms/step - loss: 0.0140 - mae: 0.0860\n",
      "Epoch 16/500\n",
      "11/11 - 0s - 29ms/step - loss: 0.0140 - mae: 0.0857\n",
      "Epoch 17/500\n",
      "11/11 - 0s - 26ms/step - loss: 0.0139 - mae: 0.0854\n",
      "Epoch 18/500\n",
      "11/11 - 0s - 22ms/step - loss: 0.0137 - mae: 0.0847\n",
      "Epoch 19/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0135 - mae: 0.0840\n",
      "Epoch 20/500\n",
      "11/11 - 0s - 31ms/step - loss: 0.0134 - mae: 0.0838\n",
      "Epoch 21/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0135 - mae: 0.0834\n",
      "Epoch 22/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0132 - mae: 0.0832\n",
      "Epoch 23/500\n",
      "11/11 - 0s - 27ms/step - loss: 0.0132 - mae: 0.0830\n",
      "Epoch 24/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0130 - mae: 0.0826\n",
      "Epoch 25/500\n",
      "11/11 - 0s - 27ms/step - loss: 0.0129 - mae: 0.0821\n",
      "Epoch 26/500\n",
      "11/11 - 0s - 29ms/step - loss: 0.0125 - mae: 0.0807\n",
      "Epoch 27/500\n",
      "11/11 - 0s - 30ms/step - loss: 0.0128 - mae: 0.0819\n",
      "Epoch 28/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0126 - mae: 0.0810\n",
      "Epoch 29/500\n",
      "11/11 - 0s - 29ms/step - loss: 0.0125 - mae: 0.0808\n",
      "Epoch 30/500\n",
      "11/11 - 0s - 29ms/step - loss: 0.0125 - mae: 0.0814\n",
      "Epoch 31/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0123 - mae: 0.0807\n",
      "Epoch 32/500\n",
      "11/11 - 0s - 34ms/step - loss: 0.0126 - mae: 0.0816\n",
      "Epoch 33/500\n",
      "11/11 - 0s - 29ms/step - loss: 0.0122 - mae: 0.0800\n",
      "Epoch 34/500\n",
      "11/11 - 0s - 31ms/step - loss: 0.0119 - mae: 0.0790\n",
      "Epoch 35/500\n",
      "11/11 - 0s - 30ms/step - loss: 0.0120 - mae: 0.0791\n",
      "Epoch 36/500\n",
      "11/11 - 0s - 29ms/step - loss: 0.0121 - mae: 0.0800\n",
      "Epoch 37/500\n",
      "11/11 - 0s - 29ms/step - loss: 0.0119 - mae: 0.0790\n",
      "Epoch 38/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0119 - mae: 0.0792\n",
      "Epoch 39/500\n",
      "11/11 - 0s - 29ms/step - loss: 0.0119 - mae: 0.0788\n",
      "Epoch 40/500\n",
      "11/11 - 0s - 30ms/step - loss: 0.0118 - mae: 0.0790\n",
      "Epoch 41/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0119 - mae: 0.0790\n",
      "Epoch 42/500\n",
      "11/11 - 0s - 30ms/step - loss: 0.0116 - mae: 0.0783\n",
      "Epoch 43/500\n",
      "11/11 - 0s - 29ms/step - loss: 0.0116 - mae: 0.0784\n",
      "Epoch 44/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0115 - mae: 0.0779\n",
      "Epoch 45/500\n",
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      "Epoch 408/500\n",
      "11/11 - 0s - 26ms/step - loss: 0.0066 - mae: 0.0591\n",
      "Epoch 409/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0066 - mae: 0.0594\n",
      "Epoch 410/500\n",
      "11/11 - 0s - 30ms/step - loss: 0.0066 - mae: 0.0595\n",
      "Epoch 411/500\n",
      "11/11 - 0s - 29ms/step - loss: 0.0066 - mae: 0.0593\n",
      "Epoch 412/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0065 - mae: 0.0591\n",
      "Epoch 413/500\n",
      "11/11 - 0s - 29ms/step - loss: 0.0068 - mae: 0.0597\n",
      "Epoch 414/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0066 - mae: 0.0593\n",
      "Epoch 415/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0067 - mae: 0.0602\n",
      "Epoch 416/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0064 - mae: 0.0586\n",
      "Epoch 417/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0064 - mae: 0.0587\n",
      "Epoch 418/500\n",
      "11/11 - 0s - 31ms/step - loss: 0.0065 - mae: 0.0589\n",
      "Epoch 419/500\n",
      "11/11 - 0s - 29ms/step - loss: 0.0065 - mae: 0.0588\n",
      "Epoch 420/500\n",
      "11/11 - 0s - 31ms/step - loss: 0.0064 - mae: 0.0582\n",
      "Epoch 421/500\n",
      "11/11 - 0s - 30ms/step - loss: 0.0067 - mae: 0.0594\n",
      "Epoch 422/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0064 - mae: 0.0586\n",
      "Epoch 423/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0069 - mae: 0.0600\n",
      "Epoch 424/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0066 - mae: 0.0595\n",
      "Epoch 425/500\n",
      "11/11 - 0s - 30ms/step - loss: 0.0064 - mae: 0.0583\n",
      "Epoch 426/500\n",
      "11/11 - 0s - 30ms/step - loss: 0.0066 - mae: 0.0598\n",
      "Epoch 427/500\n",
      "11/11 - 0s - 26ms/step - loss: 0.0064 - mae: 0.0586\n",
      "Epoch 428/500\n",
      "11/11 - 0s - 27ms/step - loss: 0.0063 - mae: 0.0587\n",
      "Epoch 429/500\n",
      "11/11 - 0s - 29ms/step - loss: 0.0065 - mae: 0.0586\n",
      "Epoch 430/500\n",
      "11/11 - 0s - 30ms/step - loss: 0.0064 - mae: 0.0588\n",
      "Epoch 431/500\n",
      "11/11 - 0s - 30ms/step - loss: 0.0065 - mae: 0.0589\n",
      "Epoch 432/500\n",
      "11/11 - 0s - 31ms/step - loss: 0.0065 - mae: 0.0589\n",
      "Epoch 433/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0065 - mae: 0.0586\n",
      "Epoch 434/500\n",
      "11/11 - 0s - 22ms/step - loss: 0.0065 - mae: 0.0586\n",
      "Epoch 435/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0064 - mae: 0.0584\n",
      "Epoch 436/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0067 - mae: 0.0589\n",
      "Epoch 437/500\n",
      "11/11 - 0s - 30ms/step - loss: 0.0062 - mae: 0.0576\n",
      "Epoch 438/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0065 - mae: 0.0588\n",
      "Epoch 439/500\n",
      "11/11 - 0s - 27ms/step - loss: 0.0063 - mae: 0.0583\n",
      "Epoch 440/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0064 - mae: 0.0585\n",
      "Epoch 441/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0062 - mae: 0.0575\n",
      "Epoch 442/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0063 - mae: 0.0580\n",
      "Epoch 443/500\n",
      "11/11 - 0s - 31ms/step - loss: 0.0063 - mae: 0.0580\n",
      "Epoch 444/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0066 - mae: 0.0593\n",
      "Epoch 445/500\n",
      "11/11 - 0s - 29ms/step - loss: 0.0066 - mae: 0.0587\n",
      "Epoch 446/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0065 - mae: 0.0591\n",
      "Epoch 447/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0064 - mae: 0.0582\n",
      "Epoch 448/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0063 - mae: 0.0583\n",
      "Epoch 449/500\n",
      "11/11 - 0s - 29ms/step - loss: 0.0064 - mae: 0.0583\n",
      "Epoch 450/500\n",
      "11/11 - 0s - 29ms/step - loss: 0.0062 - mae: 0.0577\n",
      "Epoch 451/500\n",
      "11/11 - 0s - 27ms/step - loss: 0.0064 - mae: 0.0579\n",
      "Epoch 452/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0062 - mae: 0.0578\n",
      "Epoch 453/500\n",
      "11/11 - 0s - 30ms/step - loss: 0.0065 - mae: 0.0585\n",
      "Epoch 454/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0064 - mae: 0.0585\n",
      "Epoch 455/500\n",
      "11/11 - 0s - 29ms/step - loss: 0.0063 - mae: 0.0582\n",
      "Epoch 456/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0062 - mae: 0.0576\n",
      "Epoch 457/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0064 - mae: 0.0585\n",
      "Epoch 458/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0062 - mae: 0.0578\n",
      "Epoch 459/500\n",
      "11/11 - 0s - 29ms/step - loss: 0.0063 - mae: 0.0577\n",
      "Epoch 460/500\n",
      "11/11 - 0s - 27ms/step - loss: 0.0061 - mae: 0.0576\n",
      "Epoch 461/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0062 - mae: 0.0577\n",
      "Epoch 462/500\n",
      "11/11 - 0s - 30ms/step - loss: 0.0065 - mae: 0.0592\n",
      "Epoch 463/500\n",
      "11/11 - 0s - 29ms/step - loss: 0.0066 - mae: 0.0588\n",
      "Epoch 464/500\n",
      "11/11 - 0s - 29ms/step - loss: 0.0066 - mae: 0.0592\n",
      "Epoch 465/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0065 - mae: 0.0584\n",
      "Epoch 466/500\n",
      "11/11 - 0s - 23ms/step - loss: 0.0063 - mae: 0.0580\n",
      "Epoch 467/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0065 - mae: 0.0585\n",
      "Epoch 468/500\n",
      "11/11 - 0s - 30ms/step - loss: 0.0062 - mae: 0.0579\n",
      "Epoch 469/500\n",
      "11/11 - 0s - 29ms/step - loss: 0.0062 - mae: 0.0579\n",
      "Epoch 470/500\n",
      "11/11 - 0s - 30ms/step - loss: 0.0066 - mae: 0.0591\n",
      "Epoch 471/500\n",
      "11/11 - 0s - 29ms/step - loss: 0.0060 - mae: 0.0569\n",
      "Epoch 472/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0062 - mae: 0.0578\n",
      "Epoch 473/500\n",
      "11/11 - 0s - 26ms/step - loss: 0.0062 - mae: 0.0576\n",
      "Epoch 474/500\n",
      "11/11 - 0s - 29ms/step - loss: 0.0062 - mae: 0.0574\n",
      "Epoch 475/500\n",
      "11/11 - 0s - 29ms/step - loss: 0.0062 - mae: 0.0575\n",
      "Epoch 476/500\n",
      "11/11 - 0s - 29ms/step - loss: 0.0064 - mae: 0.0581\n",
      "Epoch 477/500\n",
      "11/11 - 0s - 26ms/step - loss: 0.0065 - mae: 0.0586\n",
      "Epoch 478/500\n",
      "11/11 - 0s - 26ms/step - loss: 0.0062 - mae: 0.0576\n",
      "Epoch 479/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0061 - mae: 0.0573\n",
      "Epoch 480/500\n",
      "11/11 - 0s - 27ms/step - loss: 0.0062 - mae: 0.0575\n",
      "Epoch 481/500\n",
      "11/11 - 0s - 27ms/step - loss: 0.0063 - mae: 0.0582\n",
      "Epoch 482/500\n",
      "11/11 - 0s - 26ms/step - loss: 0.0062 - mae: 0.0575\n",
      "Epoch 483/500\n",
      "11/11 - 0s - 30ms/step - loss: 0.0063 - mae: 0.0581\n",
      "Epoch 484/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0063 - mae: 0.0582\n",
      "Epoch 485/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0062 - mae: 0.0576\n",
      "Epoch 486/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0060 - mae: 0.0567\n",
      "Epoch 487/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0060 - mae: 0.0568\n",
      "Epoch 488/500\n",
      "11/11 - 0s - 27ms/step - loss: 0.0062 - mae: 0.0575\n",
      "Epoch 489/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0062 - mae: 0.0574\n",
      "Epoch 490/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0062 - mae: 0.0575\n",
      "Epoch 491/500\n",
      "11/11 - 0s - 30ms/step - loss: 0.0063 - mae: 0.0574\n",
      "Epoch 492/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0061 - mae: 0.0578\n",
      "Epoch 493/500\n",
      "11/11 - 0s - 26ms/step - loss: 0.0063 - mae: 0.0577\n",
      "Epoch 494/500\n",
      "11/11 - 0s - 29ms/step - loss: 0.0061 - mae: 0.0574\n",
      "Epoch 495/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0064 - mae: 0.0582\n",
      "Epoch 496/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0063 - mae: 0.0583\n",
      "Epoch 497/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0062 - mae: 0.0574\n",
      "Epoch 498/500\n",
      "11/11 - 0s - 27ms/step - loss: 0.0062 - mae: 0.0576\n",
      "Epoch 499/500\n",
      "11/11 - 0s - 29ms/step - loss: 0.0062 - mae: 0.0575\n",
      "Epoch 500/500\n",
      "11/11 - 0s - 28ms/step - loss: 0.0061 - mae: 0.0574\n"
     ]
    }
   ],
   "source": [
    "history = cnn.fit(X, Y, batch_size=32, epochs=500, verbose=2,\n",
    "                     callbacks=[EarlyStopping(monitor='mae', patience=30, restore_best_weights=True)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b5470185-8778-4018-82a7-488579465e9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11/11 - 0s - 13ms/step - loss: 0.0041 - mae: 0.0470\n",
      "[0.004139313939958811, 0.047045014798641205]\n"
     ]
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 5))\n",
    "plt.plot(history.history['mae'], label='train mae')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "print(cnn.evaluate(X, Y, verbose=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "45939d56-0397-44fa-b686-1fb0de581a92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step\n"
     ]
    }
   ],
   "source": [
    "pred_tst = cnn.predict(X_tst)\n",
    "pur_sep = scaler_pur.inverse_transform(pred_tst[:, :forward].transpose())\n",
    "red_sep = scaler_red.inverse_transform(pred_tst[:, forward:].transpose())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ad582e3f-8a3a-4ce3-9650-7ec8f684eb9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# 假设 pur_sep 和 red_sep 已经被正确计算\n",
    "# 例如：\n",
    "# pur_sep = [100, 200, 300, ...]  # 只是一个示例\n",
    "# red_sep = [50, 60, 70, ...]    # 只是一个示例\n",
    "\n",
    "# 创建 DataFrame\n",
    "test_user = pd.DataFrame({\n",
    "    'report_date': [20140900 + i for i in range(1, 31)]\n",
    "})\n",
    "\n",
    "# 将 pur_sep 和 red_sep 添加到 DataFrame 中\n",
    "# 确保 pur_sep 和 red_sep 是整数类型的序列\n",
    "test_user['pur'] = pur_sep.astype('int')\n",
    "test_user['red'] = red_sep.astype('int')\n",
    "\n",
    "# 将 DataFrame 保存为 CSV 文件\n",
    "test_user.to_csv('submission.csv', encoding='utf-8', index=None, header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6e6ca5e0-606b-4e68-acff-acbd2d4ae022",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 读取CSV文件\n",
    "data = pd.read_csv('submission.csv', header=None)\n",
    "\n",
    "# 由于CSV文件中没有列名，我们可以给列命名\n",
    "data.columns = ['report_date', 'pur', 'red']\n",
    "\n",
    "# 将 'report_date' 列转换为日期类型\n",
    "data['report_date'] = pd.to_datetime(data['report_date'], format='%Y%m%d')\n",
    "\n",
    "# 绘制 'pur' 和 'red' 列的折线图\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(data['report_date'], data['pur'], label='Pur')\n",
    "plt.plot(data['report_date'], data['red'], label='Red')\n",
    "\n",
    "# 设置图表标题和标签\n",
    "plt.title('Submission Data Visualization')\n",
    "plt.xlabel('Report Date')\n",
    "plt.ylabel('Values')\n",
    "\n",
    "# 显示图例\n",
    "plt.legend()\n",
    "\n",
    "# 显示图表\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6c6a9d6-1d08-4e9a-a35a-634d5dd7eddb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
